Multivariate Statistical Analysis of Assembly Tolerance Specifications
i Multivariate Statistical Analysis of Assembly Tolerance Specifications A Thesis Presented ..........................................................................................................12 2.6.1 Multivariate Normal Distributions.8.3 Combined Assembly Specification Quality...............................................22 2.9 Multivariate
Statistics 667: Multivariate Analysis, Spring 2014 Section: 01 Index Number: 18557
Jornsten, Rebecka
Statistics 667: Multivariate Analysis, Spring 2014 Section: 01 Index Number: 18557 Room: Hill 552 of Multivariate Statistics (Textbook) by M. Bilodeau and D. Brenner (Springer, 1999). Â· Multivariate Analysis by K.V. Mardia, K.T. Kent and J.M. Bibby (Academic Press, 1979). Â· Aspects of Multivariate Statistical Theory
Multivariate Analysis from a Statistical Point of View K.S. Cranmer
Fernandez, Thomas
-Madison, Madison, WI 53706, USA Multivariate Analysis is an increasingly common tool in experimental high energy Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, most algorithms do internally is optimal for the the tasks which they perform within high energy physics
Paris-Sud XI, Université de
VIBRATION-BASED HEALTH MONITORING APPROACH FOR COMPOSITE STRUCTURES USING MULTIVARIATE STATISTICAL makes Structural Health Monitoring (SHM) a must for such materials and structures. The development of a proper structural health monitoring system has a crucial importance for such structures because
Multivariate Analysis with Linearizable Regressions
Jan de Leeuw
2011-01-01T23:59:59.000Z
13] J. de Leeuw. Multivariate analysis with linearizable1988. 14] J. de Leeuw. Multivariate analysis with optimaleditors, Progress in Multivariate Analysis , Calcutta,
Multivariate Statistical Tests for Comparing Classification Algorithms
AlpaydÃ½n, Ethem
Multivariate Statistical Tests for Comparing Classification Algorithms Olcay Taner Yildiz1 , Â¨Ozlem these in a single number, we propose to collect multivariate statistics and use multivariate tests on them rate (tpr) and false positive rate (fpr) and a multivariate test can also use such two values instead
Jiao, Jiu Jimmy
Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen August 2006; accepted 2 September 2006 Multivariate statistical analysis was used to investigate Multivariate statistical techniques are efficient ways to display complex relationships among many objects
Wisconsin at Madison, University of
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical range of such methods by deriv- ing schemes for multivariate multiple linear regression -- a manifold ] , ^ = ¯y - ^¯x. (2) If x and y are multivariates, one can easily replace the mul- tiplication and division
Multivariate Analysis with Optimal Scaling
Jan de Leeuw
2011-01-01T23:59:59.000Z
Bentler and D.G. Weeks. Multivariate Analysis with LatentB26:211–252, 1964. MULTIVARIATE ANALYSIS WITH OPTIMALGi? System of Nonlinear Multivariate Analysis. In E. Diday,
Multivariate Statistics Unit code: MATH38061
Sidorov, Nikita
MATH38061 Multivariate Statistics Unit code: MATH38061 Credit Rating: 10 Unit level: Level 3. Aims To familiarise students with the ideas and methodology of certain multivariate methods together sets of data are multivariate in that they consist of observations on several different variables
Multivariate Statistics Unit code: MATH48061
Sidorov, Nikita
MATH48061 Multivariate Statistics Unit code: MATH48061 Credit Rating: 15 Unit level: Level 4. Aims To familiarise students with the ideas and methodology of certain multivariate methods together sets of data are multivariate in that they consist of observations on several different variables
An Application of Multivariate Statistical Analysis for Query-Driven Visualization
Gosink, Luke J.; Garth, Christoph; Anderson, John C.; Bethel, E. Wes; Joy, Kenneth I.
2010-03-01T23:59:59.000Z
Abstract?Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.
NONLINEAR MULTIVARIATE AND TIME SERIES ANALYSIS BY NEURAL NETWORK METHODS
Hsieh, William
NONLINEAR MULTIVARIATE AND TIME SERIES ANALYSIS BY NEURAL NETWORK METHODS William W. Hsieh] Methods in multivariate statistical analysis are essential for working with large amounts of geophysical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base
Multivariate analysis of cross-hole georadar velocity and attenuation tomograms for aquifer zonation
Barrash, Warren
Multivariate analysis of cross-hole georadar velocity and attenuation tomograms for aquifer for characterizing heterogeneous alluvial aquifers. A multivariate statistical technique, known as k-means cluster radar, multivariate statistics, unconfined aquifers Citation: Tronicke, J., K. Holliger, W. Barrash
Multivariate analysis of neuronal interactions in the generalized partial least squares framework the brain. Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection. Here, we investigate multivariate analysis
Advanced Multivariate Analysis Tools Applied to Surface Analysis...
Office of Scientific and Technical Information (OSTI)
Advanced Multivariate Analysis Tools Applied to Surface Analysis. Citation Details In-Document Search Title: Advanced Multivariate Analysis Tools Applied to Surface Analysis. No...
Online tools for sequence retrieval and multivariate statistics in molecular biology
Thioulouse, Jean
Online tools for sequence retrieval and multivariate statistics in molecular biology Guy Perrière@biomserv.univlyon1.fr Keywords: WorldWide Web; Sequence data banks; Retrieval system; Multivariate analysis; Sequence analysis. * To whom reprint requests should be sent. #12; Abstract We have developed a World
Multivariate analysis of homogeneous nucleation rate measurements. Nucleation in the p-toluic acid. Building on these results, the powerful utility of multivariate statistical methods is demonstrated here
Method of multivariate spectral analysis
Keenan, Michael R.; Kotula, Paul G.
2004-01-06T23:59:59.000Z
A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).
Wehlau, David
Multivariate Analysis and Applications Today, due to advances in computers, massive amounts of data to the large number of variables and the interrelated nature among these variables. Multivariate statistical of basic Multivariate Analysis such as multivariate mean and variance analysis, T-Hotelling, Multinormal
Thioulouse, Jean
Thioulouse, J., D. Chessel, and S. Champely. 1995. Multivariate analysis of spatial patterns://pbil.univ-lyon1.fr/R/articles/arti092.pdf #12;Thioulouse, J., D. Chessel, and S. Champely. 1995. Multivariate, and S. Champely. 1995. Multivariate analysis of spatial patterns: a unified approach to local and global
Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge
Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery, interpretation, and presentation of multivariate spatial patterns are important for scientific understanding together to detect and visualize multivariate spatial patterns. The integrated approach is able to: (1
Classical least squares multivariate spectral analysis
Haaland, David M. (Albuquerque, NM)
2002-01-01T23:59:59.000Z
An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.
Nonlinear multivariate analysis of Neurophysiological Signals
Ernesto Pereda; Rodrigo Quian Quiroga; Joydeep Bhattacharya
2005-10-31T23:59:59.000Z
Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependences between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M. (Albuquerque, NM)
2002-01-01T23:59:59.000Z
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2004-03-23T23:59:59.000Z
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Glick, D.C.; Davis, A.
1984-07-01T23:59:59.000Z
The multivariate statistical techniques of correlation coefficients, factor analysis, and cluster analysis, implemented by computer programs, can be used to process a large data set and produce a summary of relationships between variables and between samples. These techniques were used to find relationships for data on the inorganic constituents of US coals. Three hundred thirty-five whole-seam channel samples from six US coal provinces were analyzed for inorganic variables. After consideration of the attributes of data expressed on ash basis and whole-coal basis, it was decided to perform complete statistical analyses on both data sets. Thirty variables expressed on whole-coal basis and twenty-six variables expressed on ash basis were used. For each inorganic variable, a frequency distribution histogram and a set of summary statistics was produced. These were subdivided to reveal the manner in which concentrations of inorganic constituents vary between coal provinces and between coal regions. Data collected on 124 samples from three stratigraphic groups (Pottsville, Monongahela, Allegheny) in the Appalachian region were studied using analysis of variance to determine degree of variability between stratigraphic levels. Most variables showed differences in mean values between the three groups. 193 references, 71 figures, 54 tables.
Generalized Multivariate Rank Type Test Statistics via Spatial U-Quantiles
Serfling, Robert
Generalized Multivariate Rank Type Test Statistics via Spatial U-Quantiles Weihua Zhou1 University for location have been extended over the years to the multivariate setting, including recent robust rotation invariant "spatial" versions. Here we introduce a broad class of rotation invariant multivariate spatial
PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.
Doyle, Barney Lee; Antolak, Arlyn J. (Sandia National Labs, Livermore, CA); Campbell, J. L. (University of Guelph, Guelph, ON, Canada); Ryan, C. G. (CSIRO Exploration and Mining Bayview Road, Clayton VIC, Australia); Provencio, Paula Polyak; Barrett, Keith E. (Primecore Systems, Albuquerque, NM,); Kotula, Paul Gabriel
2005-07-01T23:59:59.000Z
Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.
A Visualization Tool for Exploratory Analysis of Cyclic Multivariate Data
Ward, Matthew
A Visualization Tool for Exploratory Analysis of Cyclic Multivariate Data MATTHEW O. WARD Worcester visualization tool for the qualitaÂ tive exploration of multivariate data that may exhibit cyclic or periodic behavior. Glyphs are used to encode each multivariate data point, and linear, stacked, and spiral glyph
Multivariate periodic wavelet analysis Dirk Langemann, Jurgen Prestin
Prestin, JÃ¼rgen
Multivariate periodic wavelet analysis Dirk Langemann, JÂ¨urgen Prestin University of LÂ¨ubeck, Institute of Mathematics, Wallstr. 40, D-23560 LÂ¨ubeck, Germany Abstract General multivariate periodic are generalized to multivariate shift invariant spaces on non-tensor-product pat- terns. In particular
Multivariate Mathematical Morphology applied to Color Image Analysis
LefÃ¨vre, SÃ©bastien
Chapter 10 Multivariate Mathematical Morphology applied to Color Image Analysis 10.1. Introduction multivariate morphological operators, none of them has yet been widely accepted. Chapter written by E. APTOULA and S. LEFÃ?VRE. 303 #12;304 Multivariate Image Processing The lexicographical ordering is certainly
Augmented Classical Least Squares Multivariate Spectral Analysis
Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)
2005-01-11T23:59:59.000Z
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Augmented Classical Least Squares Multivariate Spectral Analysis
Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)
2005-07-26T23:59:59.000Z
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Augmented classical least squares multivariate spectral analysis
Haaland, David M.; Melgaard, David K.
2004-02-03T23:59:59.000Z
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
ADE SOFTWARE: MULTIVARIATE ANALYSIS AND GRAPHICAL DISPLAY OF ENVIRONMENTAL DATA
Thioulouse, Jean
ADE SOFTWARE: MULTIVARIATE ANALYSIS AND GRAPHICAL DISPLAY OF ENVIRONMENTAL DATA J. Thioulouse Name: ADE software 4.0. Date of release: 3/95. Developers: Jean Thioulouse, Daniel Chessel, Sylvain. General remarks ADE (Analysis of Environmental Data) software deals with the multivariate analysis
Stellar populations in $\\omega$ Centauri: a multivariate analysis
Fraix-Burnet, Didier
2015-01-01T23:59:59.000Z
We have performed multivariate statistical analyses of photometric and chemical abundance parameters of three large samples of stars in the globular cluster $\\omega$ Centauri. The statistical analysis of a sample of 735 stars based on seven chemical abundances with the method of Maximum Parsimony (cladistics) yields the most promising results: seven groups are found, distributed along three branches with distinct chemical, spatial and kinematical properties. A progressive chemical evolution can be traced from one group to the next, but also within groups, suggestive of an inhomogeneous chemical enrichment of the initial interstellar matter. The adjustment of stellar evolution models shows that the groups with metallicities [Fe/H]\\textgreater{}-1.5 are Helium-enriched, thus presumably of second generation. The spatial concentration of the groups increases with chemical evolution, except for two groups, which stand out in their other properties as well. The amplitude of rotation decreases with chemical evolutio...
2001, Applied Statistics, 50, 143-154. Nonlinear autoregressive time series with multivariate
Glasbey, Chris
2001, Applied Statistics, 50, 143-154. Nonlinear autoregressive time series with multivariate's Buildings, Edinburgh, EH9 3JZ, Scotland July 27, 2000 Abstract A new form of nonlinear autoregressive time to be multivariate Gaussian mixtures. The model is also a type of multiprocess dynamic linear model
On-line tools for sequence retrieval and multivariate statistics in molecular biology
Thioulouse, Jean
On-line tools for sequence retrieval and multivariate statistics in molecular biology Guy Perrière@biomserv.univ-lyon1.fr Keywords: World-Wide Web; Sequence data banks; Retrieval system; Multivariateanalysis; Sequence for browsing sequence collections structured under ACNUC format and for performing multivariate analyses
Apparatus and system for multivariate spectral analysis
Keenan, Michael R. (Albuquerque, NM); Kotula, Paul G. (Albuquerque, NM)
2003-06-24T23:59:59.000Z
An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.
Improved permeability prediction using multivariate analysis methods
Xie, Jiang
2009-05-15T23:59:59.000Z
of tree regression and cross-validation. The third is multivariate adaptive regression splines. Three methods are tested and compared at two complex carbonate reservoirs in west Texas: Salt Creek Field Unit (SCFU) and North Robertson Unit (NRU). The result...
Parish, Chad M [ORNL
2011-01-01T23:59:59.000Z
A modern scanning transmission electron microscope (STEM) fitted with an energy dispersive X-ray spectroscopy (EDS) system can quickly and easily produce spectrum image (SI) datasets containing so much information (hundreds to thousands of megabytes) that they cannot be comprehensively interrogated by a human analyst. Therefore, advanced mathematical techniques are needed to glean materials science and engineering insight into the processing-structure-properties relationship of the examined material from the SI data. This review will discuss recent advances in the application of multivariate statistical analysis (MVSA) methods to STEM-EDS SI experiments. In particular, the fundamental mathematics of principal component analysis (PCA) and related methods are reviewed, and advanced methods such as multivariate curve resolution (MCR) are discussed. The applications of PCA and MCR-based techniques to solve difficult materials science problems, such as the analysis of a particle fully embedded in a matrix phase are discussed, as well as confounding effects such as rank deficiency that can confuse the results of MVSA computations. Possible future advances and areas in need of study are also mentioned.
2011-01-01T23:59:59.000Z
spectroscopy coupled with multivariate data analysis. I.Krzanowski WJ: Principles of multivariate analysis: a user’set al. : Combining multivariate analysis and monosaccharide
2011-01-01T23:59:59.000Z
spectroscopy coupled with multivariate data analysis. I.Krzanowski WJ: Principles of multivariate analysis: a user’set al. : Combining multivariate analysis and monosaccharide
Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer's Disease
REVIEW Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently. Multivariate techniques also lend themselves much better to prospective application of results from
NetMul, a WorldWide Web user interface for multivariate analysis software
Thioulouse, Jean
1 NetMul, a WorldWide Web user interface for multivariate analysis software Jean Thioulouse The development of graphical user interfaces (GUI) for computer operating systems and desktop software Macintosh Finder, are examples of this development. Statistical software has followed this route (see
A New Multivariate Variance Components Analysis for Genetic Analysis of DTI data Agatha D. Lee1
Thompson, Paul
A New Multivariate Variance Components Analysis for Genetic Analysis of DTI data Agatha D. Lee1 sample sizes. The method may be used for both univariate and multivariate data. We applied our analysis to both univariate and multivariate (tensor) measures derived from diffusion tensor images (DTI) of 25
Statistical analysis in multispectral remote sensing
Albert, Walter Gerald
1970-01-01T23:59:59.000Z
and examines various statistical techniques for their usefulness in crop pre- diction. These techniques include multiple regression, discriminant analysis, and likelihood ratio tests. Other procedures employed in this paper are univsriate and multivariate... analysis of variance. Several transformations are performed on the data, sets in an attempt to increase accuracy for discrimination of crops, Conclu- sions of the work undertaken in this paper are presented, and recommendations are made for further...
Constantin, Alexandra Elena
2012-01-01T23:59:59.000Z
Multivariate survival11 Multivariate serial analysis of glioblastomas 11.1 Data94 11.2 Final multivariate model with significant
Software Integration for Multivariate Exploratory Spatial Data Analysis
Symanzik, JÃ¼rgen
Software Integration for Multivariate Exploratory Spatial Data Analysis JË?urgen Symanzik 1 software to support exploratory spatial data analysis (ESDA) where there are multiple measured attributes. In the first part, we review early experiments in software linking for ESDA, which used XGobi, di
De Leeuw, Jan
2012-01-01T23:59:59.000Z
and Cumulants from Multivariate Distributions. StatisticsTaylor Expan- sion of a Multivariate Function. International79(3):278–305, 1991. MULTIVARIATE CUMULANTS IN R J. Morton
Software Integration for Multivariate Exploratory Spatial Data Analysis
Symanzik, JÃ¼rgen
Software Integration for Multivariate Exploratory Spatial Data Analysis Jurgen Symanzik1, Deborah F@iastate.edu Abstract This paper describes a decade's worth of evolution of integrating software to support exploratory, we review early experiments in software linking for ESDA, which used XGobi, di erent Geographic
Software Integration for Multivariate Exploratory Spatial Data Analysis
Symanzik, JÃ¼rgen
Software Integration for Multivariate Exploratory Spatial Data Analysis JÂ¨urgen Symanzik1 , Deborah@iastate.edu Abstract This paper describes a decade's worth of evolution of integrating software to support exploratory, we review early experiments in software linking for ESDA, which used XGobi, different Geographic
A Spatial Analysis of Multivariate Output from Regional Climate Models
Sain, Steve
, Columbus, OH 43210, ncressie@stat.osu.edu. 1 #12;1 Introduction Many processes in the Earth system cannot, etc. Climate models attempt to represent this system, as well as to incorporate anthropogenic forcingsA Spatial Analysis of Multivariate Output from Regional Climate Models Stephan R. Sain,1 Reinhard
Improved permeability prediction using multivariate analysis methods
Xie, Jiang
2009-05-15T23:59:59.000Z
Predicting rock permeability from well logs in uncored wells is an important task in reservoir characterization. Due to the high costs of coring and laboratory analysis, typically cores are acquired in only a few wells. Since most wells are logged...
A multivariate analysis of Pteryxia terebinthina (Apiaceae)1 Feng-Jie Sun2,3
Downie, Stephen R.
A multivariate analysis of Pteryxia terebinthina (Apiaceae)1 Feng-Jie Sun2,3 Department of Plant Biology, 505 South Goodwin Avenue, University of Illinois, Urbana, IL 61801). A multivariate analysis. Multivariate analysis of variance, principal component analysis, and discriminant analysis of 265 specimens
Multivariate and univariate analysis of continuous arterial spin labeling perfusion MRI arterial spin labeling (CASL) magnetic resonance imaging (MRI) was combined with multivariate analysis and precuneus. When tested on extensive split-half analysis to map out the replicability of both multivariate
Multivariate Analysis of Drosophila Courtship Author(s): Therese A. Markow and Stephen J. Hanson
Markow, Therese
Multivariate Analysis of Drosophila Courtship Author(s): Therese A. Markow and Stephen J. Hanson.Natl.Acad.Sci.USA Vol. 78, No. 1, pp. 430-434, January1981 Genetics Multivariate analysis of Drosophila (sequential%). Potential ap- plications of this multivariate analysis to investigations of neuro- biological
with multivariate statistical analysis Thèse Massoud Ghasemzadeh-Barvarz Doctorat en génie chimique Philosophiae Squares (PLS), Multivariate Curve Resolution (MCR) and Multivariate Image Analysis/Multivariate Image and Multivariate Image Analysis (MIA). The potential and effectiveness of the proposed method for detecting defects
A multivariate analysis of Pseudocymopterus (Apiaceae)1 Feng-Jie Sun2
Downie, Stephen R.
A multivariate analysis of Pseudocymopterus (Apiaceae)1 Feng-Jie Sun2 Department of Plant Biology, IL 61801). A multivariate analysis of Pseudocymopterus (Apiaceae). J. Torrey Bot. Soc. 133: 499 defined taxa. Multivariate analyses of 235 specimens reflecting the morphological variability exhibited
Multivariate analysis of spatial patterns: a unified approach to local and global structures
Thioulouse, Jean
Multivariate analysis of spatial patterns: a unified approach to local and global structures JEAN. Abstract: We propose a new approach to the multivariate analysis of data sets with known sampling site and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours
Zandstra, Peter W.
Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation responses, we employ a multivariate systems analysis of proteomic data gener- ated from combinatorial-4 expression levels. This data- driven, multivariate (16 conditions 31 components 3 time points 1
A multivariate analysis of the niches of plant populations in raised bogs. I. Niche dimensions
Johnson, Edward A.
A multivariate analysis of the niches of plant populations in raised bogs. I. Niche dimensions E. A Biologictrl Lrrhorrrrory,WoorlsHole, MA, U.S.A. Received July 19, 1976 JOHNSON,E. A. 1977. A multivariate. A multivariate analysis of the niches of plant populations in raised bogs. I. Niche dimensions. Can. J. Bot. 55
Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago
O'Brien, Timothy E.
in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources. Keywords Atmospheric aerosols . Canonical correlation analysis . Chicago air pollution . Multivariate statistics . Principal component analysis . Trace gasses Introduction Many air
Yu, Qiqing
Journal of Multivariate Analysis, 1999, vol. 69, 155Â166. GENERALIZED MLE OF A JOINT DISTRIBUTION FUNCTION WITH MULTIVARIATE INTERVALÂCENSORED DATA George Y. C. Wong 1 and Qiqing Yu 2 Strang Cancer at Binghamton, NY 13902, USA, First version 6/24/97; Current version 1/28/98 Short Title: Multivariate survival
DistributionFree Multivariate Process Control Based On LogLinear Modeling School of Statistics
Qiu, Peihua
DistributionÂFree Multivariate Process Control Based On LogÂLinear Modeling Peihua Qiu School the process measurement is multivariate. In the literature, most existing multivariate SPC procedures assume that the inÂcontrol distribution of the multivariate process measurement is known and it is a Gaussian
Hennig, Christian
Football and the dark side of cluster analysis (and of exploratory multivariate analysis in general applies to the choice of map- ping and clustering method, but not treated here. 4 Football players dataset Football players characterised by 125 variables taken from whoscored.com (have > 2000 players but use only
Sloshing in the LNG shipping industry: risk modelling through multivariate heavy-tail analysis
Sloshing in the LNG shipping industry: risk modelling through multivariate heavy-tail analysis In the liquefied natural gas (LNG) shipping industry, the phenomenon of slosh- ing can lead to the occurrence in the LNG shipping industry. KEYWORDS: Sloshing, multivariate heavy-tail distribution, asymptotic depen
Shen, Haipeng
2013-01-01T23:59:59.000Z
Journal of Multivariate Analysis 115 (2013) 317333 Contents lists available at SciVerse ScienceDirect Journal of Multivariate Analysis journal homepage: www.elsevier.com/locate/jmva Consistency of sparse PCA. [21] in various multivariate analysis contexts. Conventional PCA was first studied using HDLSS
Thompson, Paul
Multivariate Statistics of Tensor-Based Cortical Surface Morphometry Y. Wang1,2, X. Gu3, T. F Chan2, United States Introduction: We propose a new set of multivariate statistics of tensor-based morphometric and 40 are from William Syndrome. We applied the multivariate statistics of tensor based morphometry
Broader source: Energy.gov (indexed) [DOE]
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5 TablesExports to3,1,50022,3,,0,,6,1,Separation 23 362Transmission:portion5WhenEnergy 3 for the State Energy30021Analysis of LM Stakeholder
Multivariate Statistical Analysis of Water Chemistry in Evaluating the
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousPlasmaP a g e OctoberEnergy FormerSites |BGE's SMARTDepartment ofRefineryTemperatureOrigin of
Random matrix approach to multivariate categorical data analysis
Patil, Aashay
2015-01-01T23:59:59.000Z
Correlation and similarity measures are widely used in all the areas of sciences and social sciences. Often the variables are not numbers but are instead qualitative descriptors called categorical data. We define and study similarity matrix, as a measure of similarity, for the case of categorical data. This is of interest due to a deluge of categorical data, such as movie ratings, top-10 rankings and data from social media, in the public domain that require analysis. We show that the statistical properties of the spectra of similarity matrices, constructed from categorical data, follow those from random matrix theory. We demonstrate this approach by applying it to the data of Indian general elections and sea level pressures in North Atlantic ocean.
Multivariate Tensor-based Brain Anatomical Surface Morphometry via Holomorphic
Thompson, Paul
Multivariate Tensor-based Brain Anatomical Surface Morphometry via Holomorphic One-Forms Yalin Wang}@loni.ucla.edu Abstract. Here we introduce multivariate tensor-based surface mor- phometry using holomorphic one abnormalities. Multivariate statistics on the local tensors outperformed other TBM methods including analysis
Systematic wavelength selection for improved multivariate spectral analysis
Thomas, Edward V. (2828 Georgia NE., Albuquerque, NM 87110); Robinson, Mark R. (1603 Solano NE., Albuquerque, NM 87110); Haaland, David M. (809 Richmond Dr. SE., Albuquerque, NM 87106)
1995-01-01T23:59:59.000Z
Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.
Author's personal copy Journal of Multivariate Analysis 100 (2009) 9931028
Meerschaert, Mark M.
2009-01-01T23:59:59.000Z
Department of Probability and Statistics, Michigan State University, East Lansing, MI 48824, United States of ground water flow and contaminant transport, essential physical properties such as hydraulic conductivity.1) where 0 measure on Rd . This random
Multiple regression on distance matrices: a multivariate spatial analysis tool Jeremy W. Lichstein
Lichstein, Jeremy W.
Multiple regression on distance matrices: a multivariate spatial analysis tool Jeremy W. Lichstein, Spatial autocorrelation Abstract I explore the use of multiple regression on distance matrices (MRM regression of a response matrix on any number of explanatory matrices, where each matrix contains distances
AstroStat - A VO Tool for Statistical Analysis
Kembhavi, Ajit K; Kale, Tejas; Jagade, Santosh; Vibhute, Ajay; Garg, Prerak; Vaghmare, Kaustubh; Navelkar, Sharmad; Agrawal, Tushar; Nandrekar, Deoyani; Shaikh, Mohasin
2015-01-01T23:59:59.000Z
AstroStat is an easy-to-use tool for performing statistical analysis on data. It has been designed to be compatible with Virtual Observatory (VO) standards thus enabling it to become an integral part of the currently available collection of VO tools. A user can load data in a variety of formats into AstroStat and perform various statistical tests using a menu driven interface. Behind the scenes, all analysis is done using the public domain statistical software - R and the output returned is presented in a neatly formatted form to the user. The analyses performable include exploratory tests, visualizations, distribution fitting, correlation & causation, hypothesis testing, multivariate analysis and clustering. The tool is available in two versions with identical interface and features - as a web service that can be run using any standard browser and as an offline application. AstroStat will provide an easy-to-use interface which can allow for both fetching data and performing power statistical analysis on ...
STATISTICAL PHONE: 530.752.2361
Wang, Jane-Ling
. from 1995 to 1998, he developed further expertise in software develop- ment, statistical programming, analysis, programming, and interpretation. Since joining the Statistical Laboratory in 2005, he has R. Beran: multivariate regression, bootstrap meth- ods, statistics on manifolds, asymptotic theory P
Spectral compression algorithms for the analysis of very large multivariate images
Keenan, Michael R. (Albuquerque, NM)
2007-10-16T23:59:59.000Z
A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM ZONGMING MA Department of Statistics University (CCA) is a widely used multivariate statistical technique for exploring the relation between two sets
Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis
Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)
2013-11-15T23:59:59.000Z
Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.
Spatial compression algorithm for the analysis of very large multivariate images
Keenan, Michael R. (Albuquerque, NM)
2008-07-15T23:59:59.000Z
A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.
Statistical analysis of correlated fossil fuel securities
Li, Derek Z
2011-01-01T23:59:59.000Z
Forecasting the future prices or returns of a security is extraordinarily difficult if not impossible. However, statistical analysis of a basket of highly correlated securities offering a cross-sectional representation of ...
Zuo, Yijun
and Scatter Estimators in Multivariate Analysis Yijun Zuo Department of Statistics and Probability, Michigan covariance matrix are the cor- ner stone of the classical multivariate analysis. They are optimal when estima- tors and discusses their applications to the multivariate data analysis. 1. Introduction
Searches for Dark Matter at the LHC: A Multivariate Analysis in the Mono-$Z$ Channel
Alexandre Alves; Kuver Sinha
2015-07-29T23:59:59.000Z
We study dark matter (DM) production in the mono-Z channel at the 13 TeV LHC both in an effective field theory framework as well as in simplified models with vector mediators, using a multivariate analysis. For DM-quark effective operators with scalar, vector, and tensor couplings and DM mass of 100 GeV, the 5$\\sigma$ reach in the DM interaction scale $\\Lambda$ is around 2, 1, and 3 TeV, respectively, for 3 ab$^{-1}$ and assuming a 5\\% systematic uncertainty on the total background normalization. For simplified models with leptophobic vector mediators, the 5$\\sigma$ reach for the mass of the mediator is 1.7 TeV also assuming a 5\\% systematics and 3 ab$^{-1}$ of integrated luminosity. The reach for the dark matter interaction scale obtained with the multivariate analysis using a likelihood function discriminant is at least twice as high as that obtained from a simple cut and count analysis, once systematics on the background normalization larger than a few percent are taken into account. Moreover, the reach is much more stable against degradation due these systematic uncertainties.
Searches for Dark Matter at the LHC: A Multivariate Analysis in the Mono-$Z$ Channel
Alves, Alexandre
2015-01-01T23:59:59.000Z
We study dark matter (DM) production in the mono-Z channel at the 13 TeV LHC both in an effective field theory framework as well as in simplified models with vector mediators, using a multivariate analysis. For DM-quark effective operators with scalar, vector, and tensor couplings and DM mass of 100 GeV, the 5$\\sigma$ reach in the DM interaction scale $\\Lambda$ is around 2, 1, and 3 TeV, respectively, for 3 ab$^{-1}$ and assuming a 5\\% systematic uncertainty on the total background normalization. For simplified models with leptophobic vector mediators, the 5$\\sigma$ reach for the mass of the mediator is 1.7 TeV also assuming a 5\\% systematics and 3 ab$^{-1}$ of integrated luminosity. The reach for the dark matter interaction scale obtained with the multivariate analysis using a likelihood function discriminant is at least twice as high as that obtained from a simple cut and count analysis, once systematics on the background normalization larger than a few percent are taken into account. Moreover, the reach is...
Statistical Design, Analysis and Graphics for the Guadalupe
Statistical Design, Analysis and Graphics for the Guadalupe River Assessment Technical Memoranda Science Center (2013). Statistical Design, Analysis and Graphics for the Guadalupe River Assessment...................................................................................................... 7 Study Design
Meta-Analysis for Longitudinal Data Models using Multivariate Mixture Priors
West, Mike
of multivariate normals, accomodating population heterogeneity, out- liers and non-linearity in regression. First, the random e#11;ects model is a exible mixture of multivariate normals, accomodating population
STATISTICAL ANALYSIS OF PROTEIN FOLDING KINETICS
Dinner, Aaron
STATISTICAL ANALYSIS OF PROTEIN FOLDING KINETICS AARON R. DINNER New Chemistry Laboratory for Protein Folding: Advances in Chemical Physics, Volume 120. Edited by Richard A. Friesner. Series Editors Experimental and theoretical studies have led to the emergence of a unified general mechanism for protein
A. Recio-Blanco; A. Aparicio; G. Piotto; F. De Angeli; S. G. Djorgovski
2005-11-24T23:59:59.000Z
The interpretation of globular cluster horizontal branch (HB) morphology is a classical problem that can significantly blur our understanding of stellar populations. In this paper, we present a new multivariate analysis connecting the effective temperature extent of the HB with other cluster parameters. The work is based on Hubble Space Telescope photometry of 54 Galactic globular clusters. The present study reveals an important role of the total mass of the globular cluster on its HB morphology. More massive clusters tend to have HBs more extended to higher temperatures. For a set of three input variables including the temperature extension of the HB, [Fe/H] and M_V, the first two eigenvectors account for the 90% of the total sample variance. Possible effects of cluster self-pollution on HB morphology, eventually stronger in more massive clusters, could explain the results here derived.
Multivariate Standardized Drought Index: A parametric multi-index model
Hao, Zengchao; AghaKouchak, Amir
2013-01-01T23:59:59.000Z
G, De Michele C. Multivariate multiparameter extreme valueL, Thiémonge N, Bobée B. Multivariate hydrological frequencyfrequency analysis of multivariate hydrological data. Water
Stanford University
Multivariate analysis and prediction of wind turbine response to varying wind field characteristics characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine
On quantum statistics in data analysis
Dusko Pavlovic
2008-05-13T23:59:59.000Z
Originally, quantum probability theory was developed to analyze statistical phenomena in quantum systems, where classical probability theory does not apply, because the lattice of measurable sets is not necessarily distributive. On the other hand, it is well known that the lattices of concepts, that arise in data analysis, are in general also non-distributive, albeit for completely different reasons. In his recent book, van Rijsbergen argues that many of the logical tools developed for quantum systems are also suitable for applications in information retrieval. I explore the mathematical support for this idea on an abstract vector space model, covering several forms of data analysis (information retrieval, data mining, collaborative filtering, formal concept analysis...), and roughly based on an idea from categorical quantum mechanics. It turns out that quantum (i.e., noncommutative) probability distributions arise already in this rudimentary mathematical framework. We show that a Bell-type inequality must be satisfied by the standard similarity measures, if they are used for preference predictions. The fact that already a very general, abstract version of the vector space model yields simple counterexamples for such inequalities seems to be an indicator of a genuine need for quantum statistics in data analysis.
Cloud-Based Statistical Analysis from Users' Perspective Botong Huang
Yang, Jun
efficient statistical analysis programs requires tremendous expertise and effort. Most statisticians would much prefer programming in languages familiar to them, such as R and MATLAB, Copyright 2014 IEEECumulon: Cloud-Based Statistical Analysis from Users' Perspective Botong Huang Department
A Convex Hull Peeling Depth Approach to Nonparametric Massive Multivariate Data
Wolfe, Patrick J.
A Convex Hull Peeling Depth Approach to Nonparametric Massive Multivariate Data Analysis) and Multivariate Data Analysis Definitions on CHP Data Depth (Ordering Multivariate Data) Quantiles and Density with CHP Multivariate Median Skewness and Kurtosis of a Multivariate Distribution Outlier Detection
Non resonant transmission modelling with Statistical modal Energy distribution Analysis
Boyer, Edmond
be used as an alternative to Statistical Energy Analysis for describing subsystems with low modal overlap1 Non resonant transmission modelling with Statistical modal Energy distribution Analysis L. Maxit Capelle, F-69621 Villeurbanne Cedex, France Statistical modal Energy distribution Analysis (SmEdA) can
Statistical Hot Channel Analysis for the NBSR
Cuadra A.; Baek J.
2014-05-27T23:59:59.000Z
A statistical analysis of thermal limits has been carried out for the research reactor (NBSR) at the National Institute of Standards and Technology (NIST). The objective of this analysis was to update the uncertainties of the hot channel factors with respect to previous analysis for both high-enriched uranium (HEU) and low-enriched uranium (LEU) fuels. Although uncertainties in key parameters which enter into the analysis are not yet known for the LEU core, the current analysis uses reasonable approximations instead of conservative estimates based on HEU values. Cumulative distribution functions (CDFs) were obtained for critical heat flux ratio (CHFR), and onset of flow instability ratio (OFIR). As was done previously, the Sudo-Kaminaga correlation was used for CHF and the Saha-Zuber correlation was used for OFI. Results were obtained for probability levels of 90%, 95%, and 99.9%. As an example of the analysis, the results for both the existing reactor with HEU fuel and the LEU core show that CHFR would have to be above 1.39 to assure with 95% probability that there is no CHF. For the OFIR, the results show that the ratio should be above 1.40 to assure with a 95% probability that OFI is not reached.
Statistics for Analysis of Experimental Data Catherine A. Peters
Peters, Catherine A.
Statistics for Analysis of Experimental Data Catherine A. Peters Department of Civil Engineering Processes Laboratory Manual S. E. Powers, Ed. AEESP, Champaign, IL 2001 1 #12;Statistics Princeton University Princeton, NJ 08544 Statistics is a mathematical tool for quantitative analysis of data
Goutami Chattopadhyay; Surajit Chattopadhyay; Rajni Jain
2009-10-28T23:59:59.000Z
In this paper, the complexities in the relationship between rainfall and sea surface temperature (SST) anomalies during the winter monsoon (November-January) over India were evaluated statistically using scatter plot matrices and autocorrelation functions.Linear as well as polynomial trend equations were obtained and it was observed that the coefficient of determination for the linear trend was very low and it remained low even when polynomial trend of degree six was used. An exponential regression equation and an artificial neural network with extensive variable selection were generated to forecast the average winter monsoon rainfall of a given year using the rainfall amounts and the sea surface temperature anomalies in the winter monsoon months of the previous year as predictors. The regression coefficients for the multiple exponential regression equation were generated using Levenberg-Marquardt algorithm. The artificial neural network was generated in the form of a multiplayer perceptron with sigmoid non-linearity and genetic-algorithm based variable selection. Both of the predictive models were judged statistically using the Willmott index, percentage error of prediction, and prediction yields. The statistical assessment revealed the potential of artificial neural network over exponential regression.
Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring
Dayman, Kenneth J. [Univ. of Texas at Austin, TX (United States); Coble, Jamie B. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Orton, Christopher R. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Schwantes, Jon M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
2014-01-01T23:59:59.000Z
The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic used fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this work are artificially low, because the models were trained, optimized, and tested on simulated, noise-free data. However, these results indicate that the developed models may generalize well to new data and that the proposed approach constitutes a viable first step in developing a fuel characterization algorithm based on gamma spectra.
Experimental control analysis of a fuel gas saturator. Final report. [Multivariable
Terwilliger, G.E.; Brower, A.S.; Baheti, R.S.; Smith, R.E.; Brown, D.H.
1985-01-01T23:59:59.000Z
The multivariable control of the clean fuel gas saturator of a coal gasification process has been demonstrated. First principle process models described the process dynamics from which linear models were generated and used for the actual control designs. The multivariable control was designed, its response to transients simulated and the controls were implemented in a computer controller for a fuel gas saturator. The test results obtained for the gas flow transients showed good correlation with the computer simulations, giving confidence in the ability of the simulation to predict the plant performance for other transients. In this study, both time and frequency domain multivariable design techniques were applied to provide the best possible design and to determine their relative effectiveness. No clear guidelines resulted; it appears that the selection may be made on the basis of personal preference, experience or the availability of computer-aided design tools, rather than inherent technical differences. This EPRI/GE fuel gas saturator control demonstration has shown that multivariable design techniques can be applied to a real process and that practical controls are developed. With suitable process models, presently available computer-aided control design software allows the control design, evaluation and implementation to be completed in a reasonable time period. The application of these techniques to power generation processes is recommended.
* corresponding author A Statistical Analysis of Student Design Projects
Sobek II, Durward K.
* corresponding author A Statistical Analysis of Student Design Projects: Helping Good Design activity. A regression analysis indicates that approximately 60% of the variance in design outcome can positively with design quality. The data codification and analysis procedures are presented
Seismic Attribute Analysis Using Higher Order Statistics
Greenidge, Janelle Candice
2009-05-15T23:59:59.000Z
Seismic data processing depends on mathematical and statistical tools such as convolution, crosscorrelation and stack that employ second-order statistics (SOS). Seismic signals are non-Gaussian and therefore contain information beyond SOS. One...
A MULTIVARIATE OUTLIER DETECTION P. Filzmoser
Filzmoser, Peter
A MULTIVARIATE OUTLIER DETECTION METHOD P. Filzmoser Department of Statistics and Probability of multivariate outliers is proposed which accounts for the data structure and sample size. The cut-off value no distinction is made between outliers and extremes of a distribution. The basis for multivariate outlier
Multivariate Change Detection in Multispectral, Multitemporal Images
Multivariate Change Detection in Multispectral, Multitemporal Images Knut Conradsen Allan Aasbjerg, Building 321 DK-2800 Lyngby, Denmark Abstract This paper introduces a new orthogonal transfonn the multivariate change detection (MeD) transfonn based on an established multivariate statistical tech- nique
Multivariate analysis of exhaust emissions from heavy-duty diesel fuels
Sjoegren, M.; Ulf, R.; Li, H.; Westerholm, R. [Stockholm Univ. (Sweden)
1996-01-01T23:59:59.000Z
Particulate and gaseous exhaust emission phases from running 10 diesel fuels on two makes of heavy-duty diesel engines were analyzed with respect to 63 chemical descriptors. Measurements for one of the fuels were also made in the presence of an exhaust aftertreatment device. The variables included 28 polycyclic aromatic compounds (PAC), regulated pollutants (CO, HC, NO{sub x}, particles), and 19 other organic and inorganic exhaust emission components. Principal components analysis (PCA) was applied for the statistical exploration of the obtained data. In addition, relationships between chemical (12 variables) and physical (12 variables) parameters of the fuels to the exhaust emissions were derived using partial least squares (PLS) regression. Both PCA and PLS models were derived for the engine makes separately. The PCA showed that the most descriptive exhaust emission factors from these diesel fuels included fluoranthene as a representative of PAC, the regulated pollutants, sulfates, methylated pyrenes, and monoaromatics. Exhaust emissions were significantly decreased in the presence of an exhaust aftertreatment device. Both engine makes exhibited similar patterns of exhaust emissions. Discrepancies were observed for the exhaust emissions of CO{sub 2} and oil-derived soluble organic fractions, owing to differences in engine design. The PLS analysis showed a good correlation of exhaust emission of the regulated pollutants and PAC with the contents of PAC in the fuels and the fuel aromaticity. 41 refs., 6 figs., 6 tabs.
Palombo, Giulio
2011-01-01T23:59:59.000Z
High Energy Physics data sets are often characterized by a huge number of events. Therefore, it is extremely important to use statistical packages able to efficiently analyze these unprecedented amounts of data. We compare the performance of the statistical packages StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory usage of the learning process scale versus data set size. As classifiers, we consider Random Forests, Boosted Decision Trees and Neural Networks. For our tests, we employ a data set widely used in the machine learning community, "Threenorm" data set, as well as data tailored for testing various edge cases. For each data set, we constantly increase its size and check CPU time and memory needed to build the classifiers implemented in SPR and TMVA. We show that SPR is often significantly faster and consumes significantly less memory. For example, the SPR implementation of Random Forest is by an order of magnitude faster and consumes an order...
Giulio Palombo
2011-03-28T23:59:59.000Z
High Energy Physics data sets are often characterized by a huge number of events. Therefore, it is extremely important to use statistical packages able to efficiently analyze these unprecedented amounts of data. We compare the performance of the statistical packages StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory usage of the learning process scale versus data set size. As classifiers, we consider Random Forests, Boosted Decision Trees and Neural Networks. For our tests, we employ a data set widely used in the machine learning community, "Threenorm" data set, as well as data tailored for testing various edge cases. For each data set, we constantly increase its size and check CPU time and memory needed to build the classifiers implemented in SPR and TMVA. We show that SPR is often significantly faster and consumes significantly less memory. For example, the SPR implementation of Random Forest is by an order of magnitude faster and consumes an order of magnitude less memory than TMVA on Threenorm data.
Statistical Analysis of Transient Cycle Test Results in a 40...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Analysis of Transient Cycle Test Results in a 40 CFR Part 1065 Engine Dynamometer Test Cell Statistical Analysis of Transient Cycle Test Results in a 40 CFR Part 1065 Engine...
Electricity Case: Statistical Analysis of Electric Power Outages
Wang, Hai
Electricity Case: Statistical Analysis of Electric Power Outages CREATE Report Jeffrey S. Simonoff: Statistical Analysis of Electric Power Outages CREATE Report July 26, 2005 Jeffrey S. Simonoff (NYU of the United States Department of Homeland Security. #12;0 Electricity Case, Report 3 Electricity Case
TREATMENT OF MULTIVARIATE ENVIRONMENTAL AND HEALTH PROBLEMS ASSOCIATED WITH OIL SHALE TECHNOLOGY
Kland, M.J.
2010-01-01T23:59:59.000Z
Applic.ation of Multivariate Analysis for Interpretation ofthe Proceedings TREATMENT OF MULTIVARIATE ENVIRONMENTAL ANDENG-48 TREATMENT OF MULTIVARIATE ENVIRONMENTAL AND HEALTH
Understanding Manufacturing Energy Use Through Statistical Analysis
Kissock, J. K.; Seryak, J.
2004-01-01T23:59:59.000Z
Energy in manufacturing facilities is used for direct production of goods, space conditioning, and general facility support such as lighting. This paper presents a methodology for statistically analyzing plant energy use in terms of these major end...
Parameterization and Statistical Analysis of Hurricane Waves
Mclaughlin, Patrick William
2014-05-03T23:59:59.000Z
the JPM-OS methodology yielded extreme value statistics for 179 stations of interest. Maps detailing the spatial extents of the 100 and 1000 year maximum wave event were created using ArcGIS....
VIBROACOUSTICS AT HIGH FREQUENCIES Coupling of Statistical Energy Analysis and numerical methods
Huerta, Antonio
VIBROACOUSTICS AT HIGH FREQUENCIES Coupling of Statistical Energy Analysis and numerical methods at high frequencies. Instead, a statistical approach (Statistical Energy Analysis) can be used: Deterministic methods: expensive and too detailed at high frequencies. Statistical methods: require energy
XIAOQIAN SUN As of January 2014 Associate Professor of Statistics
Sun, Xiaoqian
Random Effects Model", Communications in Statistics Theory and Methods, accepted for publication 4. M theory, Bayesian statistics, High dimensional statistical inference, Multivariate analysis, Spatial Matrices in a Star-shaped Model with Missing Data", Communica- tions in Statistics, Theory & Methods, 39
Multivariate Input Models for Stochastic Simulation Michael E. Kuhl
Kuhl, Michael E.
Multivariate Input Models for Stochastic Simulation Michael E. Kuhl Department of Industrial are presented for modeling and randomly sampling many of the multivariate probabilistic input processes decision analysis illustrates the proposed technique. Also discussed is a multivariate extension
A statistical analysis of lead concentrations in human lung samples
Stringer, Claude Allen
1973-01-01T23:59:59.000Z
A STATISTICAL ANALYSIS OF LEAD CONCENTRATIONS IN HUMAN LUNG SAMPLES A Thesis by CLAUDE ALLEN STRINGER, JR. Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER... OF SCIENCE May 1973 Major Subject: Chemistry A STATISTICAL ANALYSIS OF LEAD CONCENTRATIONS IN HUMAN LUNG SAMPLES A Thesis CLAUDE ALLEN STRINGER, JR. Approved as to style and content by: (Chairman of Commi e) (Head of Department) C Member (Memb...
Overview of multivariate methods and their application to studies of wildlife habitat
Shugart, H.H. Jr.
1980-01-01T23:59:59.000Z
Multivariate statistical techniques as methods of choice in analyzing habitat relations among animals have distinct advantages over competitive methodologies. These considerations, joined with a reduction in the cost of computer time, the increased availability of multivariate statistical packages, and an increased willingness on the part of ecologists to use mathematics and statistics as tools, have created an exponentially increasing interest in multivariate statistical methods over the past decade. It is important to note that the earliest multivariate statistical analyses in ecology did more than introduce a set of appropriate and needed methodologies to ecology. The studies emphasized different spatial and organizational scales from those typically emphasized in habitat studies. The new studies, that used multivariate methods, emphasized individual organisms' responses in a heterogeneous environment. This philosophical (and to some degree, methodological) emphasis on heterogeneity has led to a potential to predict the consequences of disturbances and management on wildlife habitat. One recent development in this regard has been the coupling of forest succession simulators with multivariate analysis of habitat to predict habitat availability under different timber management procedures.
Course STAT 6348.501 Applied Multivariate Analysis Professor Robert Serfling
Serfling, Robert
Analysis. The Classification Problem: Discriminant Analysis, Support Vector Machines, Classification the professor's notes accompanied by handouts distributed by email and from library eBooks. Also, other sources). T 11/15 R 11/17 Classification: discriminant analysis; support vector machines. Support vector machines
Probabilistic and Statistical Analysis of Perforated Patterns
Misailovic, Sasa
2011-01-19T23:59:59.000Z
We present a new foundation for the analysis and transformation of computer programs.Standard approaches involve the use of logical reasoning to prove that the applied transformation does not change the observable semantics ...
Ras, Zbigniew W.
Multivariate Visual Explanation for High Dimensional Datasets Scott Barlowe, Tianyi Zhang, Yujie at Charlotte djacobs1@uncc.edu ABSTRACT Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate vi- sualization systems lose effectiveness
Beadle, Sarah
2014-08-30T23:59:59.000Z
. Cluster analysis was performed on a novel dataset, collated from previous Belizean land use studies. Analysis of this dataset found that Belizean lowland savanna soil is generally acidic (mean surface pH=5.45) and nutrient poor (mean surface TEB=3.4cmol...
Robust Regression Analysis: Some Popular Statistical Package Options
Masci, Frank
1 Robust Regression Analysis: Some Popular Statistical Package Options By Robert A. Yaffee Meyer of SAS, Institute for their gracious assistance. Robust regression analysis provides an alternative to a least squares regression model when fundamental assumptions are unfulfilled by the nature
Parameterization and Statistical Analysis of Hurricane Waves
Mclaughlin, Patrick William
2014-05-03T23:59:59.000Z
improvements ranging from 0.13-0.32 m. Once WRF coefficients are adjust to minimize RMSE at each station under consideration, extreme value analysis via the Joint Probability Method with Optimal Sampling (JPM-OS) was conducted. When applied to Panama City, FL...
A statistical analysis of personnel contaminations in 200 Area facilities
Wagner, M.A.; Stoddard, D.H.
1983-05-18T23:59:59.000Z
This study determined the frequency statistics of personnel contaminations in 200 Area facilities. These statistics are utilized in probability calculations for contamination risks, and are part of an effort to provide reliable information for use in safety studies. Data for this analysis were obtained from the 200 Area and the Tritium Area Fault Tree Data Banks and were analyzed with the aid of the STATPAC computer code.
Technical University of Denmark Richard Petersens Plads, Building 321 DK-2800 Kongens Lyngby, Denmark aa of multivariate data including · principal components, · principal factors, · (multi-set) canonical variates components. All these methods are useful in exploratory multivariate data analysis where we consider
Multivariate Analysis of Longitudinal Ordinal Data with Mixed Eects Models, with Application to
to Clinical Outcomes in Osteoarthritis Celine Marielle Laont1,2, Marc Vandemeulebroecke3, Didier Concordet1 are used. Typically, four ordinal outcomes are measured in clinical trials, including the posture of a dog feature in clinical trials. However, the standard methods for data analysis use unidimen- sional models
Nonparametric Finite Multivariate
Hunter, David
Nonparametric Finite Multivariate Mixture Models with Applications Xiaotian Zhu Introduction-Minimization #12;Nonparametric Finite Multivariate Mixture Models with Applications Xiaotian Zhu Introduction-Minimization #12;Nonparametric Finite Multivariate Mixture Models with Applications Xiaotian Zhu Introduction
Statistical Error analysis of Nucleon-Nucleon phenomenological potentials
R. Navarro Perez; J. E. Amaro; E. Ruiz Arriola
2014-06-10T23:59:59.000Z
Nucleon-Nucleon potentials are commonplace in nuclear physics and are determined from a finite number of experimental data with limited precision sampling the scattering process. We study the statistical assumptions implicit in the standard least squares fitting procedure and apply, along with more conventional tests, a tail sensitive quantile-quantile test as a simple and confident tool to verify the normality of residuals. We show that the fulfilment of normality tests is linked to a judicious and consistent selection of a nucleon-nucleon database. These considerations prove crucial to a proper statistical error analysis and uncertainty propagation. We illustrate these issues by analyzing about 8000 proton-proton and neutron-proton scattering published data. This enables the construction of potentials meeting all statistical requirements necessary for statistical uncertainty estimates in nuclear structure calculations.
Data analysis using the Gnu R system for statistical computation
Simone, James; /Fermilab
2011-07-01T23:59:59.000Z
R is a language system for statistical computation. It is widely used in statistics, bioinformatics, machine learning, data mining, quantitative finance, and the analysis of clinical drug trials. Among the advantages of R are: it has become the standard language for developing statistical techniques, it is being actively developed by a large and growing global user community, it is open source software, it is highly portable (Linux, OS-X and Windows), it has a built-in documentation system, it produces high quality graphics and it is easily extensible with over four thousand extension library packages available covering statistics and applications. This report gives a very brief introduction to R with some examples using lattice QCD simulation results. It then discusses the development of R packages designed for chi-square minimization fits for lattice n-pt correlation functions.
Fractional Factorial Experiments Statistical Design and Analysis of Experiments p.1/14
Conati, Cristina
Fractional Factorial Experiments Statistical Design and Analysis of Experiments p.1/14 #12. Statistical Design and Analysis of Experiments p.2/14 #12;Confounding Statistical Design and Analysis) -(y211 +s2)-(y212 +s2)] = ¯y·2· - ¯y·1· is unconfounded. Statistical Design and Analysis
UNDERSTANDING MANUFACTURING ENERGY USE THROUGH STATISTICAL ANALYSIS KELLY KISSOCK AND JOHN SERYAK
Kissock, Kelly
data points that are relatively easy for most plants to obtain. Multivariable change- point models, pioneered the practice of automatically fitting variable-base degree-day (VBDD) models to residential utility data (Fels, 1986; Fels et al. 1995). PRISM employs three primary types of statistical models
Principal Components Analysis for Binary Data
Lee, Seokho
2010-07-14T23:59:59.000Z
Principal components analysis (PCA) has been widely used as a statistical tool for the dimension reduction of multivariate data in various application areas and extensively studied in the long history of statistics. One of the limitations of PCA...
Introduction to Statistical Linear Models Spring 2005
of multivariate data and in the language of matrices and vectors. Broad introduction to MATLAB/Octave, R (SSyllabus Introduction to Statistical Linear Models 960:577:01 Spring 2005 Instructor: Farid Statistical Analysis" Fifth edition, Prentice Hall, 2002. Other sources may be required and will be posted
Statistical Analysis of Protein Folding Kinetics Aaron R. Dinner
Dinner, Aaron
Statistical Analysis of Protein Folding Kinetics Aaron R. Dinner , Sung-Sau So ¡ , and Martin and theoretical studies over several years have led to the emergence of a unified general mechanism for protein folding that serves as a framework for the design and interpretation of research in this area [1
STATISTICAL ANALYSIS AND STRUCTURE OPTIMIZATION OF LARGE PHOTOVOLTAIC MODULE
Qiu, Qinru
STATISTICAL ANALYSIS AND STRUCTURE OPTIMIZATION OF LARGE PHOTOVOLTAIC MODULE RATHEESH R on the output power of large Photovoltaic (PV) module by modeling each PV cell as a current source whose short. Photovoltaic (PV) is a simple and elegant method of harnessing the sun's energy. PV devices (solar cells
Stochastics and Statistics Bayesian semiparametric analysis for a single item
Popova, Elmira
Stochastics and Statistics Bayesian semiparametric analysis for a single item maintenance and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA b Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712, USA c
Damping Estimation of Plates for Statistical Energy Analysis
Vatti, Kranthi
2011-06-01T23:59:59.000Z
.R.D.M. algorithm. Statistical Energy Analysis (S.E.A.), which is a natural extension of the Power Input Method, is used to evaluate coupling loss factors for two sets of plates, one set joined along a line and the other set joined at a point. Two alternative...
Recurrence time statistics: Versatile tools for genomic DNA sequence analysis
Gao, Jianbo
Recurrence time statistics: Versatile tools for genomic DNA sequence analysis Yinhe Cao1, Wen, and the genomes of many other organisms waiting to be sequenced, it has become increasingly important to develop from DNA sequences. One of the more important structures in a DNA se- quence is repeat-related. Often
Statistical Analysis of Particle Distributions in Composite Materials
Wichert, Sofia
Statistical Analysis of Particle Distributions in Composite Materials by Sofia Mucharreira de Distributions in Composite Materials Sofia Mucharreira de Azeredo Lopes Summary Particulate composite materials distributions is of prime importance for a better control of the production of particulate composite materials
Statistical Analysis of X-ray Speckle at the NSLS
Ophelia K. C. Tsui; S. G. J. Mochrie; L. E. Berman
1997-09-30T23:59:59.000Z
We report a statistical analysis of the static speckle produced by illuminating a disordered aerogel sample by a nominally coherent x-ray beam at wiggler beamline X25 at the National Synchrotron Light Source. The results of the analysis allow us to determine that the coherence delivered to the X25 hutch is within 35% of what is expected. The rate of coherent photons is approximately two times smaller than expected on the basis of the X25 wiggler source brilliance.
Water O?H Stretching Raman Signature for Strong Acid Monitoring via Multivariate Analysis
Casella, Amanda J.; Levitskaia, Tatiana G.; Peterson, James M.; Bryan, Samuel A.
2013-04-16T23:59:59.000Z
Spectroscopic techniques have been applied extensively for quantification and analysis of solution compositions. In addition to static measurements, these techniques have been implemented in flow systems providing real-time solution information. A distinct need exists for information regarding acid concentration as it affects extraction efficiency and selectivity of many separation processes. Despite of the seeming simplicity of the problem, no practical solution has been offered yet particularly for the large-scale schemes involving toxic streams such as highly radioactive nuclear wastes. Classic potentiometric technique is not amiable for on-line measurements in nuclear fuel reprocessing due to requirements of frequent calibration/maintenance and poor long-term stability in the aggressive chemical and radiation environments. In this work, the potential of using Raman spectroscopic measurements for on-line monitoring of strong acid concentration in the solutions relevant to the dissolved used fuel was investigated. The Raman water signature was monitored and recorded for nitric and hydrochloric acid solution systems of systematically varied chemical composition, ionic strength, and temperature. The generated Raman spectroscopic database was used to develop predictive chemometric models for the quantification of the acid concentration (H+), neodymium concentration (Nd3+), nitrate concentration (NO3-), density, and ionic strength. This approach was validated using a flow solvent extraction system.
Multivariate SVD Analyses For Network Anomaly Detection Lingsong Zhang
Shen, Haipeng
Multivariate SVD Analyses For Network Anomaly Detection Lingsong Zhang Haipeng Shen Zhengyuan Zhu components analysis on multivariate data rather than univariate data. A multivariate approach allows us by the multivariate outliers, being more representative of anomalous behavior. We can define the signifi- cance
Feature-Based Statistical Analysis of Combustion Simulation Data
Bennett, J; Krishnamoorthy, V; Liu, S; Grout, R; Hawkes, E; Chen, J; Pascucci, V; Bremer, P T
2011-11-18T23:59:59.000Z
We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.
Multivariate Analysis I (Honors)
Course Description. Credit Hours: 3.00. Topics covered may include a unified modern treatment of functions of several variables. Topics covered include the ...
HistFitter software framework for statistical data analysis
M. Baak; G. J. Besjes; D. Cote; A. Koutsman; J. Lorenz; D. Short
2014-10-06T23:59:59.000Z
We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fitted to data and interpreted with statistical tests. A key innovation of HistFitter is its design, which is rooted in core analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its very fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple data models at once, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication-quality style through a simple command-line interface.
Lifetime statistics of quantum chaos studied by a multiscale analysis
Di Falco, A.; Krauss, T. F. [School of Physics and Astronomy, University of St. Andrews, North Haugh, St. Andrews, KY16 9SS (United Kingdom); Fratalocchi, A. [PRIMALIGHT, Faculty of Electrical Engineering, Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900 (Saudi Arabia)
2012-04-30T23:59:59.000Z
In a series of pump and probe experiments, we study the lifetime statistics of a quantum chaotic resonator when the number of open channels is greater than one. Our design embeds a stadium billiard into a two dimensional photonic crystal realized on a silicon-on-insulator substrate. We calculate resonances through a multiscale procedure that combines energy landscape analysis and wavelet transforms. Experimental data is found to follow the universal predictions arising from random matrix theory with an excellent level of agreement.
1 Introduction Towards Better Graphics for Multivariate Anal-
Thioulouse, Jean
1 Introduction Summary Keywords: Towards Better Graphics for Multivariate Anal- ysis computed by multivariate analysis meth- ods is often a dicult task. The main diculties come from of rows and columns of the data table on the factor map. Multivariate analysis, exploratory data analysis
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM INGRAM OLKIN Department of Statistics Stanford probability, statistics, combinatorics and graphs, numerical analysis and matrix theory. Special emphasis
Statistical Analysis of Abnormal Electric Power Grid Behavior
Ferryman, Thomas A.; Amidan, Brett G.
2010-10-30T23:59:59.000Z
Pacific Northwest National Laboratory is developing a technique to analyze Phasor Measurement Unit data to identify typical patterns, atypical events and precursors to a blackout or other undesirable event. The approach combines a data-driven multivariate analysis with an engineering-model approach. The method identifies atypical events, provides a plane English description of the event, and the capability to use drill-down graphics for detailed investigations. The tool can be applied to the entire grid, individual organizations (e.g. TVA, BPA), or specific substations (e.g., TVA_CUMB). The tool is envisioned for (1) event investigations, (2) overnight processing to generate a Morning Report that characterizes the previous days activity with respect to previous activity over the previous 10-30 days, and (3) potentially near-real-time operation to support the grid operators. This paper presents the current status of the tool and illustrations of its application to real world PMU data collected in three 10-day periods in 2007.
Rubin, Herman [bio] Professor of Statistics and Mathematics PhD: University of Chicago 1948. Office: MATH 550; Phone: +1 765 49-46054; Email: ...
Grujicic, Mica
of Polymer Electrolyteand Optimization of Polymer Electrolyte Membrane (PEM) Fuel Cells,Membrane (PEM) FuelStatistical Sensitivity Analysis of the Polymer Electrolyte Membrane Fuel CellsPolymer Electrolyte Membrane Fuel Cells) Fuel Cell e- e- e- Load e- e- e- O2 O2 O2 O2 H2 H2 H2 H2 INTERDIGITATEDFUEL DISTRIBUTOR H+ OHeHO 22 244
Multivariate approximation Robert Schaback
Schaback, Robert
Multivariate approximation Robert Schaback July 30, 2013 1 Synonyms Approximation by functions Approximations of functions are multivariate, if they replace functions of n 2 variables defined on a domain. 4 Overview Multivariate approximation is an extension of Approximation Theory and Approximation
Wheeler, Conrad, and Figliozzi 1 A Statistical Analysis of Bicycle Rider Performance
Bertini, Robert L.
Wheeler, Conrad, and Figliozzi 1 A Statistical Analysis of Bicycle Rider Performance: The impact) A Statistical Analysis of Bicycle Rider Performance: The impact of gender on riders' performance at signalized;Wheeler, Conrad, and Figliozzi 2 A Statistical Analysis of Bicycle Rider Performance: The impact of gender
1 Introduction Towards Better Graphics for Multivariate Anal
Thioulouse, Jean
1 Introduction Summary Keywords: Towards Better Graphics for Multivariate Anal ysis computed by multivariate analysis meth ods is often a difficult task. The main difficulties come from, zooming, and plotting of rows and columns of the data table on the factor map. Multivariate analysis
A multivariate spatial interpolation of airborne -ray data using the geological constraints
Roma "La Sapienza", Università di
A multivariate spatial interpolation of airborne -ray data using the geological constraints Enrico: Multivariate analysis Airborne -ray spectrometry Collocated cokriging interpolator Elba Island Natural (Italy) obtained with a multivariate spatial interpolation of airborne -ray data using the constraints
Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.; Casella, Amanda J.; Peterman, Dean; Bryan, Samuel A.
2013-11-05T23:59:59.000Z
In liquid-liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness and frequent solvent analysis is warranted. Our research explores feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutyl phosphoric acid (HDBP) was assessed. Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to the high dose external gamma irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.
Tatiana G. Levitskaia; James M. Peterson; Emily L. Campbell; Amanda J. Casella; Dean R. Peterman; Samuel A. Bryan
2013-12-01T23:59:59.000Z
In liquid–liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness, and frequent solvent analysis is warranted. Our research explores the feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutylphosphoric acid (HDBP) was assessed. Fourier transform infrared (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to high-dose external ?-irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus, demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.
Statistical analysis of cascading failures in power grids
Chertkov, Michael [Los Alamos National Laboratory; Pfitzner, Rene [Los Alamos National Laboratory; Turitsyn, Konstantin [Los Alamos National Laboratory
2010-12-01T23:59:59.000Z
We introduce a new microscopic model of cascading failures in transmission power grids. This model accounts for automatic response of the grid to load fluctuations that take place on the scale of minutes, when optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, caused by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systems consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.
Statistical 2D and 3D shape analysis using Non-Euclidean Metrics
Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark {rl, kbh, proj76}@imm.dtu.dk, http For the analysis and interpretation of multivariate observations a standard meth- ods has been the application appearance models [6]. Bookstein proposed using bending energy and inverse bending energy as metrics
Statistical shape analysis using Non-Euclidean Rasmus Larsen a, Klaus Baggesen Hilger a
a a Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321 of multivariate observations a standard method has been the application of principal component analysis (PCA appearance models (6). Bookstein proposed using bending energy and inverse bending energy as metrics
Li, Haijun
Multivariate Extremes Dependence Comparison Stochastic Tail Order Back to Multivariate Extremes Dependence Comparison of Multivariate Extremes Haijun Li Department of Mathematics Washington State University IWAP12, Jerusalem Haijun Li Dependence Comparison of Multivariate Extremes IWAP12, Jerusalem 1
Statistics Math Emphasis College of Science STAT-BS
Kihara, Daisuke
Statistics Math Emphasis College of Science STAT-BS Code-STMA Departmental/Program Major Courses And Applications (3) Math Selective I: MA 36200 Topics In Vector Calculus/MA 44200 - Multivariate Analysis I Honors ******************************************************************************************************************************** #12;Revised 2/2013 (effective Fall 2013) Statistics-Math Emphasis http://www.math
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM DAVID BLEI Department of Statistics and Computer posterior inference algorithms have revolutionized Bayesian statistics, revealing its potential as a usable and general-purpose language for data analysis. Bayesian statistics, however, has not yet reached
10-10:50 am, Location Green Center 265 Web Page: The username and password for the website, economics, education, ecology, geology, sociology, energy, atmospheric sciences, law enforcement
Statistical analysis of test data for APM rod issue
Edwards, T.B.; Harris, S.P.; Reeve, C.P.
1992-05-01T23:59:59.000Z
The uncertainty associated with the use of the K-Reactor axial power monitors (APMs) to measure roof-top-ratios is investigated in this report. Internal heating test data acquired under both DC-flow conditions and AC-flow conditions have been analyzed. These tests were conducted to simulate gamma heating at the lower power levels planned for reactor operation. The objective of this statistical analysis is to investigate the relationship between the observed and true roof-top-ratio (RTR) values and associated uncertainties at power levels within this lower operational range. Conditional on a given, known power level, a prediction interval for the true RTR value corresponding to a new, observed RTR is given. This is done for a range of power levels. Estimates of total system uncertainty are also determined by combining the analog-to-digital converter uncertainty with the results from the test data.
Multivariate Tests Based on Left-Spherically Distributed Linear Scores
Magdeburg, UniversitÃ¤t
Multivariate Tests Based on Left-Spherically Distributed Linear In this paper, a method for multivariate testing based on low-dimensional, data- dependent, linear scores approaches. In a natural way, standard problems of multivari- ate analysis thus induce the occurrence
Scatterplot3d an R package for Visualizing Multivariate Data
Gotelli, Nicholas J.
Scatterplot3d an R package for Visualizing Multivariate Data Uwe Ligges and Martin M Software: Ligges, U. and M¨achler, M. (2003): Scatterplot3d an R Package for Visualizing Multivariate for the visualization of multivariate data in a three dimensional space. R is a "language for data analysis and graphics
Multivariate and Supervised Approaches for Mathematical Morphology in Remote Sensing
LefÃ¨vre, SÃ©bastien
Multivariate and Supervised Approaches for Mathematical Morphology in Remote Sensing SÂ´ebastien Lef, Strasbourg / France lefevre@lsiit.u-strasbg.fr JRC, Ispra February 28th, 2008 #12;Introduction Multivariate toolbox for image analysis but . . . How to extend it to multivariate data such as colour
Survey on (Some) Nonparametric and Robust Multivariate Methods
Serfling, Robert
Survey on (Some) Nonparametric and Robust Multivariate Methods Robert Serfling University of Texas) #12;CONTENTS Survey on (Some) Nonparametric and Robust Multivariate Methods i Goals, and an Apology Multivariate Inference and Analysis. My sincere apologies for: limited topics, unlimited omissions, limited
Statistical Analysis Of Tank 5 Floor Sample Results
Shine, E. P.
2012-08-01T23:59:59.000Z
Sampling has been completed for the characterization of the residual material on the floor of Tank 5 in the F-Area Tank Farm at the Savannah River Site (SRS), near Aiken, SC. The sampling was performed by Savannah River Remediation (SRR) LLC using a stratified random sampling plan with volume-proportional compositing. The plan consisted of partitioning the residual material on the floor of Tank 5 into three non-overlapping strata: two strata enclosed accumulations, and a third stratum consisted of a thin layer of material outside the regions of the two accumulations. Each of three composite samples was constructed from five primary sample locations of residual material on the floor of Tank 5. Three of the primary samples were obtained from the stratum containing the thin layer of material, and one primary sample was obtained from each of the two strata containing an accumulation. This report documents the statistical analyses of the analytical results for the composite samples. The objective of the analysis is to determine the mean concentrations and upper 95% confidence (UCL95) bounds for the mean concentrations for a set of analytes in the tank residuals. The statistical procedures employed in the analyses were consistent with the Environmental Protection Agency (EPA) technical guidance by Singh and others [2010]. Savannah River National Laboratory (SRNL) measured the sample bulk density, nonvolatile beta, gross alpha, and the radionuclide, elemental, and chemical concentrations three times for each of the composite samples. The analyte concentration data were partitioned into three separate groups for further analysis: analytes with every measurement above their minimum detectable concentrations (MDCs), analytes with no measurements above their MDCs, and analytes with a mixture of some measurement results above and below their MDCs. The means, standard deviations, and UCL95s were computed for the analytes in the two groups that had at least some measurements above their MDCs. The identification of distributions and the selection of UCL95 procedures generally followed the protocol in Singh, Armbya, and Singh [2010]. When all of an analyte's measurements lie below their MDCs, only a summary of the MDCs can be provided. The measurement results reported by SRNL are listed in Appendix A, and the results of this analysis are reported in Appendix B. The data were generally found to follow a normal distribution, and to be homogenous across composite samples.
STATISTICAL ANALYSIS OF TANK 5 FLOOR SAMPLE RESULTS
Shine, E.
2012-03-14T23:59:59.000Z
Sampling has been completed for the characterization of the residual material on the floor of Tank 5 in the F-Area Tank Farm at the Savannah River Site (SRS), near Aiken, SC. The sampling was performed by Savannah River Remediation (SRR) LLC using a stratified random sampling plan with volume-proportional compositing. The plan consisted of partitioning the residual material on the floor of Tank 5 into three non-overlapping strata: two strata enclosed accumulations, and a third stratum consisted of a thin layer of material outside the regions of the two accumulations. Each of three composite samples was constructed from five primary sample locations of residual material on the floor of Tank 5. Three of the primary samples were obtained from the stratum containing the thin layer of material, and one primary sample was obtained from each of the two strata containing an accumulation. This report documents the statistical analyses of the analytical results for the composite samples. The objective of the analysis is to determine the mean concentrations and upper 95% confidence (UCL95) bounds for the mean concentrations for a set of analytes in the tank residuals. The statistical procedures employed in the analyses were consistent with the Environmental Protection Agency (EPA) technical guidance by Singh and others [2010]. Savannah River National Laboratory (SRNL) measured the sample bulk density, nonvolatile beta, gross alpha, radionuclide, inorganic, and anion concentrations three times for each of the composite samples. The analyte concentration data were partitioned into three separate groups for further analysis: analytes with every measurement above their minimum detectable concentrations (MDCs), analytes with no measurements above their MDCs, and analytes with a mixture of some measurement results above and below their MDCs. The means, standard deviations, and UCL95s were computed for the analytes in the two groups that had at least some measurements above their MDCs. The identification of distributions and the selection of UCL95 procedures generally followed the protocol in Singh, Armbya, and Singh [2010]. When all of an analyte's measurements lie below their MDCs, only a summary of the MDCs can be provided. The measurement results reported by SRNL are listed in Appendix A, and the results of this analysis are reported in Appendix B. The data were generally found to follow a normal distribution, and to be homogeneous across composite samples.
Statistical Analysis of Tank 5 Floor Sample Results
Shine, E. P.
2013-01-31T23:59:59.000Z
Sampling has been completed for the characterization of the residual material on the floor of Tank 5 in the F-Area Tank Farm at the Savannah River Site (SRS), near Aiken, SC. The sampling was performed by Savannah River Remediation (SRR) LLC using a stratified random sampling plan with volume-proportional compositing. The plan consisted of partitioning the residual material on the floor of Tank 5 into three non-overlapping strata: two strata enclosed accumulations, and a third stratum consisted of a thin layer of material outside the regions of the two accumulations. Each of three composite samples was constructed from five primary sample locations of residual material on the floor of Tank 5. Three of the primary samples were obtained from the stratum containing the thin layer of material, and one primary sample was obtained from each of the two strata containing an accumulation. This report documents the statistical analyses of the analytical results for the composite samples. The objective of the analysis is to determine the mean concentrations and upper 95% confidence (UCL95) bounds for the mean concentrations for a set of analytes in the tank residuals. The statistical procedures employed in the analyses were consistent with the Environmental Protection Agency (EPA) technical guidance by Singh and others [2010]. Savannah River National Laboratory (SRNL) measured the sample bulk density, nonvolatile beta, gross alpha, and the radionuclide1, elemental, and chemical concentrations three times for each of the composite samples. The analyte concentration data were partitioned into three separate groups for further analysis: analytes with every measurement above their minimum detectable concentrations (MDCs), analytes with no measurements above their MDCs, and analytes with a mixture of some measurement results above and below their MDCs. The means, standard deviations, and UCL95s were computed for the analytes in the two groups that had at least some measurements above their MDCs. The identification of distributions and the selection of UCL95 procedures generally followed the protocol in Singh, Armbya, and Singh [2010]. When all of an analyte's measurements lie below their MDCs, only a summary of the MDCs can be provided. The measurement results reported by SRNL are listed, and the results of this analysis are reported. The data were generally found to follow a normal distribution, and to be homogenous across composite samples.
STATISTICAL AND 3D NONLINEAR FINITE ELEMENT ANALYSIS OF SCHLEGEIS DAM
Balaji, Rajagopalan
STATISTICAL AND 3D NONLINEAR FINITE ELEMENT ANALYSIS OF SCHLEGEIS DAM VICTOR SAOUMA, ERIC HANSEN is composed of two parts. First a statistical analysis of the dam crest displacement is performed, along with a prediction for the years 2000-2001. Then a 3D finite element analysis of Schlegeis dam is performed using
Montana, University of
of statistical costs associated with alternative survey scenarios. We used the program, TRENDS (Gerrodette 1987 with the definition of r); for variable 8, we assumed that the t statistic was most appropriate, given that we1 An analysis of the tradeoffs in statistical power among annual, biennial, and triennial landbird
Aires, Filipe
2003-01-01T23:59:59.000Z
to classical linear feedback analysis, we present a nonlinear approach for the determination. Such an approach is valid in a theoretical model where the instantaneous sensitivities can be evaluated directly of statistical estimates of all the pair-wise relationships among the system state variables based on a neural
Farquharson, Colin G.
in Numerical Analysis, Scientific Computing or Computational and Applied Geophysics Applications are invited University of Newfoundland Centre for Numerical Analysis and Scientific Computing, within the DepartmentMemorial University, Department of Mathematics and Statistics Postdoctoral Research Position
Clegg, Samuel M [Los Alamos National Laboratory; Wiens, Roger C. [Los Alamos National Laboratory; Speicher, Elly A [MT HOLYOKE COLLEGE; Dyar, Melinda D [MT HOLYOKE COLLEGE; Carmosino, Marco L [MT HOLYOKE COLLEGE
2010-12-23T23:59:59.000Z
Laser-induced breakdown spectroscopy (LIBS) will be employed by the ChemCam instrument on the Mars Science Laboratory rover Curiosity to obtain UV, VIS, and VNIR atomic emission spectra of surface rocks and soils. LIBS quantitative analysis is complicated by chemical matrix effects related to abundances of neutral and ionized species in the resultant plasma, collisional interactions within plasma, laser-to-sample coupling efficiency, and self-absorption. Atmospheric composition and pressure also influence the intensity of LIBS plasma. These chemical matrix effects influence the ratio of intensity or area of a given emission line to the abundance of the element producing that line. To compensate for these complications, multivariate techniques, specifically partial least-squares regression (PLS), have been utilized to predict major element compositions (>1 wt.% oxide) of rocks, PLS methods regress one or multiple response variables (elemental concentrations) against multiple explanatory variables (intensity at each pixel of the spectrometers). Because PLS utilizes all available explanatory variable and eliminates multicollinearity, it generally performs better than univariate methods for prediction of major elements. However, peaks arising from emissions from trace elements may be masked by peaks of higher intensities from major elements. Thus in PLS regression, wherein a correlation coefficient is determined for each elemental concentration at each spectrometer pixel, trace elements may show high correlation with more intense lines resulting from optical emissions of other elements. This could result in error in predictions of trace element concentrations. Here, results of simple linear regression (SLR) and multivariate PLS-2 regression for determination of trace Rb, Sr, Cr, Ba, and V in igneous rock samples are compared. This study focuses on comparisons using only line intensities rather than peak areas to highlight differences between SLR and PLS.
Fienberg, Stephen E.
. FIENBERG, MICHAEL M. MEYER, and STANLEY S. WASSERMAN* Loglinear models are adapted for the analysisStatistical Analysis of Multiple Sociometric Relations Stephen E. Fienberg; Michael M. Meyer; Stanley S. Wasserman Journal of the American Statistical Association, Vol. 80, No. 389. (Mar., 1985), pp
A model of the statistical power of comparative genome sequence analysis
Eddy, Sean
A model of the statistical power of comparative genome sequence analysis Sean R. Eddy Howard Hughes genome sequence analysis is powerful, but sequencing genomes is expensive. It is desirable to be able to predict how many genomes are needed to achieve a particular statistical power in comparative analyses
Parallel and Statistical Analysis and Modeling of Nanometer VLSI Systems
Liu, Xue-Xin
2013-01-01T23:59:59.000Z
for reduced order analysis of linear circuit with multipleWorst case analysis of linear analog circuit performancelinear analog circuits under parameter variations by robust interval analysis.
Monolithic or hierarchical star formation? A new statistical analysis
Marios Kampakoglou; Roberto Trotta; Joe Silk
2007-11-23T23:59:59.000Z
We consider an analytic model of cosmic star formation which incorporates supernova feedback, gas accretion and enriched outflows, reproducing the history of cosmic star formation, metallicity, supernovae type II rates and the fraction of baryons allocated to structures. We present a new statistical treatment of the available observational data on the star formation rate and metallicity that accounts for the presence of possible systematics. We then employ a Bayesian Markov Chain Monte Carlo method to compare the predictions of our model with observations and derive constraints on the 7 free parameters of the model. We find that the dust correction scheme one chooses to adopt for the star formation data is critical in determining which scenario is favoured between a hierarchical star formation model, where star formation is prolonged by accretion, infall and merging, and a monolithic scenario, where star formation is rapid and efficient. We distinguish between these modes by defining a characteristic minimum mass, M > 10^{11} M_solar, in our fiducial model, for early type galaxies where star formation occurs efficiently. Our results indicate that the hierarchical star formation model can achieve better agreement with the data, but that this requires a high efficiency of supernova-driven outflows. In a monolithic model, our analysis points to the need for a mechanism that drives metal-poor winds, perhaps in the form of supermassive black hole-induced outflows. Furthermore, the relative absence of star formation beyond z ~ 5 in the monolithic scenario requires an alternative mechanism to dwarf galaxies for reionizing the universe at z ~ 11, as required by observations of the microwave background. While the monolithic scenario is less favoured in terms of its quality-of-fit, it cannot yet be excluded.
Rutledge, Steven
-depth analysis using radar products, such as Doppler-derived wind vectors and hydrometeor identification, has in real time. This study focuses on modifying and automating several radar- analysis and quality of intense rainfall, hail, strong updrafts, and other features such as mesocyclones and convergence lines
Statistical analysis and transfer of coarse-grain pictorial style
Bae, Soonmin
2005-01-01T23:59:59.000Z
We show that image statistics can be used to analyze and transfer simple notions of pictorial style of paintings and photographs. We characterize the frequency content of pictorial styles, such as multi-scale, spatial ...
Application of statistical learning theory to plankton image analysis
Hu, Qiao, Ph. D. Massachusetts Institute of Technology
2006-01-01T23:59:59.000Z
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected ...
Thompson, Paul
MULTIVARIATE TENSOR-BASED MORPHOMETRY ON SURFACES: APPLICATION TO MAPPING VENTRICULAR CHANGES@stat.wisc.edu, {arthur.toga, thompson}@loni.ucla.edu ABSTRACT We apply multivariate tensor-based morphometry to study abnormalities. Multivariate Hotelling's T2 statistics on the local Riemannian metric tensors, computed in a log
West, Mike
classification, validation, prognosis Binary regression models · Linear regression model based on regression Standard statistical models transform from real-value to (0, 1) using a specified non-linear functionStatistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification
Statistical analysis of motion contrast in optical coherence tomography angiography
Cheng, Yuxuan; Pan, Cong; Lu, Tongtong; Hong, Tianyu; Ding, Zhihua; Li, Peng
2015-01-01T23:59:59.000Z
Optical coherence tomography angiography (Angio-OCT), mainly based on the temporal dynamics of OCT scattering signals, has found a range of potential applications in clinical and scientific researches. In this work, based on the model of random phasor sums, temporal statistics of the complex-valued OCT signals are mathematically described. Statistical distributions of the amplitude differential (AD) and complex differential (CD) Angio-OCT signals are derived. The theories are validated through the flow phantom and live animal experiments. Using the model developed in this work, the origin of the motion contrast in Angio-OCT is mathematically explained, and the implications in the improvement of motion contrast are further discussed, including threshold determination and its residual classification error, averaging method, and scanning protocol. The proposed mathematical model of Angio-OCT signals can aid in the optimal design of the system and associated algorithms.
Statistical analysis of illiquidity risk and premium in financial price signals
Khandani, Amir E. (Amir Ehsan), 1979-
2009-01-01T23:59:59.000Z
Price is the most visible signal produced by competition and interaction among a complex ecology of entities in a system called financial markets. This thesis deals with statistical analysis and model identification based ...
ANALYSIS OF STATISTICS FOR GENERALIZED STIRLING PERMUTATIONS MARKUS KUBA AND ALOIS PANHOLZER
Panholzer, Alois
ANALYSIS OF STATISTICS FOR GENERALIZED STIRLING PERMUTATIONS MARKUS KUBA AND ALOIS PANHOLZER ABSTRACT. In this work we give a study of generalizations of Stirling permutations, a restricted class between such generalized Stirling permutations and various families of increasing trees extending
Statistical analysis of wind energy in Chile David Watts a,b,*, Danilo Jara a
Catholic University of Chile (Universidad Católica de Chile)
Data Bank Statistical analysis of wind energy in Chile David Watts a,b,*, Danilo Jara-takers. Wind energy rates as a possible alternative which, while com- plementing other energy options, could
Fazzio, Thomas J. (Thomas Joseph)
2010-01-01T23:59:59.000Z
This paper attempts to understand the price dynamics of the North American natural gas market through a statistical survey that includes an analysis of the variables influencing the price and volatility of this energy ...
Smyth, Gordon K.
Statistics in Microarray Analysis 111 111 From: Methods in Molecular Biology: vol. 224: Functional, NJ 9 Statistical Issues in cDNA Microarray Data Analysis Gordon K. Smyth,Yee Hwa Yang, and Terry Speed 1. Introduction Statistical considerations are frequently to the fore in the analysis
Statistical and risk analysis for the measured and predicted axial response of 100 piles
Perdomo, Dario
1986-01-01T23:59:59.000Z
. , Universidad de los Andes Chairman of Advisory Committee: Dr. Jean-Louis Briaud A statistical analysis of the ultimate load and load-settlement results obtained by Briaud et al(1985) was performed . A risk analy- sis method was also developed to study... the importance of properly selecting safety factors. The data base of 100 piles was statistical- ly analyzed under five different soil categories, and several predic- tion methods were used to study the performance of the risk analysis method. In general...
Development of a thermobalance and analysis of lignites by thermogravimetric and statistical methods
Ferguson, James Allen
1984-01-01T23:59:59.000Z
DEVELOPMENT OF A THERMOBALANCE AND ANALYSIS OF LIGNITES BY THERMOGRAVIMETRIC AND STATISTICAL METHODS A Thesis by JAMES ALLEN PERGUSON Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirements... for the degree of MASTER OP SCIENCE December 1984 Major Subject: Chemistry DEVELOPMENT OF A THERMOBALANCE ANALYSIS OF LIGNITES BY THERMOGRAVIMETRIC AND STATISTICAL METHODS A Thesis by JAMES ALLEN FERGUSON Approved as to style and content by: Marv n W...
University of Illinois at Chicago; Montana State University; Bhardwaj, Chhavi; Cui, Yang; Hofstetter, Theresa; Liu, Suet Yi; Bernstein, Hans C.; Carlson, Ross P.; Ahmed, Musahid; Hanley, Luke
2013-04-01T23:59:59.000Z
7.87 to 10.5 eV vacuum ultraviolet (VUV) photon energies were used in laser desorption postionization mass spectrometry (LDPI-MS) to analyze biofilms comprised of binary cultures of interacting microorganisms. The effect of photon energy was examined using both tunable synchrotron and laser sources of VUV radiation. Principal components analysis (PCA) was applied to the MS data to differentiate species in Escherichia coli-Saccharomyces cerevisiae coculture biofilms. PCA of LDPI-MS also differentiated individual E. coli strains in a biofilm comprised of two interacting gene deletion strains, even though these strains differed from the wild type K-12 strain by no more than four gene deletions each out of approximately 2000 genes. PCA treatment of 7.87 eV LDPI-MS data separated the E. coli strains into three distinct groups two ?pure? groups and a mixed region. Furthermore, the ?pure? regions of the E. coli cocultures showed greater variance by PCA when analyzed by 7.87 eV photon energies than by 10.5 eV radiation. Comparison of the 7.87 and 10.5 eV data is consistent with the expectation that the lower photon energy selects a subset of low ionization energy analytes while 10.5 eV is more inclusive, detecting a wider range of analytes. These two VUV photon energies therefore give different spreads via PCA and their respective use in LDPI-MS constitute an additional experimental parameter to differentiate strains and species.
Internet Data Analysis for the Undergraduate Statistics Curriculum
Juana Sanchez; Yan He
2011-01-01T23:59:59.000Z
Haythornthwaite edts. (2002). The Internet in Everyday Life.Where Mathematics meets the Internet. Notices of the AMS,Internet Data Analysis for the Undergraduate Statitics
Multivariate Forecast Evaluation And Rationality Testing
Komunjer, Ivana; OWYANG, MICHAEL
2007-01-01T23:59:59.000Z
Economy, 95(5), 1062—1088. MULTIVARIATE FORECASTS Chaudhuri,Notion of Quantiles for Multivariate Data,” Journal of thePress, United Kingdom. MULTIVARIATE FORECASTS Kirchgässner,
Essays on Multivariate Modeling in Financial Econometrics
Yoldas, Emre
2008-01-01T23:59:59.000Z
79 2.2.1 Multivariate Normal74 2.2 Multivariate Contours and Autocontours . . . . . . .43 x CHAPTER II: MULTIVARIATE AUTOCONTOURS FOR SPECIFICATION
SACI: Statistical Static Timing Analysis of Coupled Interconnects
Pedram, Massoud
in the circuit timing that stem from various sources of variations. However, static timing analysis (STA crosstalk effects in these circuits. As a result, crosstalk analysis and management have been classified line as a linear function of random variables and then use these r.v.'s to compute the circuit mo
Statistical analysis of sampling methods in quantum tomography
Thomas Kiesel
2012-06-07T23:59:59.000Z
In quantum physics, all measured observables are subject to statistical uncertainties, which arise from the quantum nature as well as the experimental technique. We consider the statistical uncertainty of the so-called sampling method, in which one estimates the expectation value of a given observable by empirical means of suitable pattern functions. We show that if the observable can be written as a function of a single directly measurable operator, the variance of the estimate from the sampling method equals to the quantum mechanical one. In this sense, we say that the estimate is on the quantum mechanical level of uncertainty. In contrast, if the observable depends on non-commuting operators, e.g. different quadratures, the quantum mechanical level of uncertainty is not achieved. The impact of the results on quantum tomography is discussed, and different approaches to quantum tomographic measurements are compared. It is shown explicitly for the estimation of quasiprobabilities of a quantum state, that balanced homodyne tomography does not operate on the quantum mechanical level of uncertainty, while the unbalanced homodyne detection does.
Journal club Multivariate Signal integration
Journal club Multivariate Signal integration A fundamental aspect of biological systems is that they are multivariate: cells receive, integrate and respond to hundreds or thousands of concurrent environmental cues in the context of the cell's multivariate network state. Because this depends on cues in the environment
Defect site prediction based upon statistical analysis of fault signatures
Trinka, Michael Robert
2004-09-30T23:59:59.000Z
Good failure analysis is the ability to determine the site of a circuit defect quickly and accurately. We propose a method for defect site prediction that is based on a site's probability of excitation, making no assumptions about the type...
Indoor air quality: multivariate analyses of the relationship between indoor and outdoor aerosols
McCarthy, S.M.
1986-01-01T23:59:59.000Z
A unique multivariate data set incorporating simultaneous indoor and outdoor measurements of sixteen air contaminants at ten homes has been used to investigate the contribution of outdoor concentrations to indoor aerosol variability, and to characterize indoor source contribution to the indoor concentrations. The data were available from an earlier field study of particle and gas concentrations outside and inside five homes in each of two cities: Portage, Wisconsin, and Steubenville, Ohio. Three distinct multivariate statistical techniques were used sequentially in the research, successively building on the results and interpretations as they developed. Cluster analysis was selected as the initial method for partitioning the variables into subgroups comprised of highly intercorrelated variables. Significant site-to-site variability was evident in both cities, however within sites, indoor clusters had similarities to the outdoor clusters. Principal component analysis was next performed on the Portage data, reduced in dimension to avoid problems of singularity in the data matrix. The principal component analyses results were used to attribute predominant indoor and outdoor sources, including cigarette smoke, wood stove, road dust, and urban combustion sources. Finally, multiple regression analysis was performed to relate outdoor pollutant concentrations to a composite index of the indoor aerosol as represented by the orthogonal rotations of the indoor principal components. The research indicates that this multivariate analysis framework is preferable to single univariate analysis in evaluating the influence of outdoor aerosols and indoor sources on indoor air quality data.
Quantile tomography: using quantiles with multivariate data,
Jin, Jiashun
Quantile tomography: using quantiles with multivariate data, with applications to multivariate at smooth boundary points. They can be viewed as a natural, nonparametric extension of "multivariate quantiles" yielded by fitted multivariate normal distribution, and, as illustrated on data examples
Multivariate Counting Processes By Mathias Zocher
Schmidt, Klaus D.
Multivariate Counting Processes By Mathias Zocher Fachrichtung Mathematik Technische Universit the recent development of the transition from univariate models to multivariate models, this paper considers multivariate counting processes. Multivariate versions of the Poisson process and the mixed Poisson process
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM ERIC KOLACZYK Department of Statistics Boston University Statistical Analysis of Network Data: (Re)visiting the Foundations MONDAY, October 13, 2014, at 4, statistical methods and modeling have been central to these efforts. But how well do we truly understand
ECOGRAPHY 25: 553557, 2002 Integrating the statistical analysis of spatial data in ecology
Liebhold, Andrew
ECOGRAPHY 25: 553557, 2002 Integrating the statistical analysis of spatial data in ecology A. M of spatial data in ecology. Ecography 25: 553557. In many areas of ecology there is an increasing emphasis for the analysis of spatial data has yielded considerable insight into various ecological problems, this diversity
Perturbing Numerical Calculations for Statistical Analysis of Floating-Point Program (In)Stability
Su, Zhendong
mathematical techniques for performing error and stability analysis of numerical algorithms. However to numerical computing tend to suffer from misconceptions that arise from thinking in terms of R, not itsPerturbing Numerical Calculations for Statistical Analysis of Floating-Point Program (In
A Model of the Statistical Power of Comparative Genome Sequence Analysis
Eddy, Sean
A Model of the Statistical Power of Comparative Genome Sequence Analysis Sean R. Eddy Howard Hughes, Missouri, United States of America Comparative genome sequence analysis is powerful, but sequencing genomes is expensive. It is desirable to be able to predict how many genomes are needed for comparative genomics
Sezgin, Metin
2006-01-01T23:59:59.000Z
in the binary expansions of Feigenbaum constants a and d for the logistic map. The analysis is carried out constants; Normal numbers; Random number generation; Statistical analysis In a recent article Karamanos their conclusions. For example in the first table the calculated w2 value is 1.421 as stated. But this value fails
Lawrence, Rick L.
Statistics programs1 teach individuals to apply mathematical principles to the collection, analysis and statistical knowledge to the design of surveys and experiments; collection, processing, and analysis of data; and interpretation of the results. Statisticians may apply their knowledge of statistical methods to a variety
Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.
1981-01-01T23:59:59.000Z
A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained.
Statistical language analysis for automatic exfiltration event detection.
Robinson, David Gerald
2010-04-01T23:59:59.000Z
This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.
U.S. Energy Information Administration Independent Statistics & Analysis
U.S. Energy Information Administration (EIA) Indexed Site
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page onsource History View NewUS NationalStocks 2009 2010 2011 2012Presented0.1Kingdom6,25.6 40.7200Coalâ€¹ Analysis
U.S. Energy Information Administration Independent Statistics & Analysis
U.S. Energy Information Administration (EIA) Indexed Site
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page onsource History View NewUS NationalStocks 2009 2010 2011 2012Presented0.1Kingdom6,25.6 40.7200Coalâ€¹ AnalysisJune
Analysis of compressive fracture in rock using statistical techniques
Blair, S.C.
1994-12-01T23:59:59.000Z
Fracture of rock in compression is analyzed using a field-theory model, and the processes of crack coalescence and fracture formation and the effect of grain-scale heterogeneities on macroscopic behavior of rock are studied. The model is based on observations of fracture in laboratory compression tests, and incorporates assumptions developed using fracture mechanics analysis of rock fracture. The model represents grains as discrete sites, and uses superposition of continuum and crack-interaction stresses to create cracks at these sites. The sites are also used to introduce local heterogeneity. Clusters of cracked sites can be analyzed using percolation theory. Stress-strain curves for simulated uniaxial tests were analyzed by studying the location of cracked sites, and partitioning of strain energy for selected intervals. Results show that the model implicitly predicts both development of shear-type fracture surfaces and a strength-vs-size relation that are similar to those observed for real rocks. Results of a parameter-sensitivity analysis indicate that heterogeneity in the local stresses, attributed to the shape and loading of individual grains, has a first-order effect on strength, and that increasing local stress heterogeneity lowers compressive strength following an inverse power law. Peak strength decreased with increasing lattice size and decreasing mean site strength, and was independent of site-strength distribution. A model for rock fracture based on a nearest-neighbor algorithm for stress redistribution is also presented and used to simulate laboratory compression tests, with promising results.
Multivariate Approaches to Classification in Extragalactic Astronomy
Fraix-Burnet, Didier; Chattopadhyay, Asis Kumar
2015-01-01T23:59:59.000Z
Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
The statistical analysis techniques to support the NGNP fuel performance experiments
Binh T. Pham; Jeffrey J. Einerson
2013-10-01T23:59:59.000Z
This paper describes the development and application of statistical analysis techniques to support the Advanced Gas Reactor (AGR) experimental program on Next Generation Nuclear Plant (NGNP) fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel temperature) is regulated by the He–Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the NGNP Data Management and Analysis System for automated processing and qualification of the AGR measured data. The neutronic and thermal code simulation results are used for comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the fuel temperature within a given range.
Multivariate characterization of hydrogen Balmer emission in cataclysmic variables
Gordon E. Sarty; Kinwah Wu
2006-08-18T23:59:59.000Z
The ratios of hydrogen Balmer emission line intensities in cataclysmic variables are signatures of the physical processes that produce them. To quantify those signatures relative to classifications of cataclysmic variable types, we applied the multivariate statistical analysis methods of principal components analysis and discriminant function analysis to the spectroscopic emission data set of Williams (1983). The two analysis methods reveal two different sources of variation in the ratios of the emission lines. The source of variation seen in the principal components analysis was shown to be correlated with the binary orbital period. The source of variation seen in the discriminant function analysis was shown to be correlated with the equivalent width of the H$\\beta$ line. Comparison of the data scatterplot with scatterplots of theoretical models shows that Balmer line emission from T CrB systems is consistent with the photoionization of a surrounding nebula. Otherwise, models that we considered do not reproduce the wide range of Balmer decrements, including "inverted" decrements, seen in the data.
DOE/OR-1066R5/02-03 7-1 7. DATA ANALYSIS AND STATISTICAL TREATMENT
Pennycook, Steve
DOE/OR-1066R5/02-03 7-1 7. DATA ANALYSIS AND STATISTICAL TREATMENT 7.1 INTRODUCTION Four goals for analysis and statistical treatment of data are identified in Chap. 7 of the Regulatory Guide. These goals and nonradiological monitoring programs through a general discussion of the DQO process, data analysis
Kockelman, Kara M.
1 1 Relaxing the Multivariate Normality Assumption in the Simulation 2 of Transportation System network analysis literature is the3 use of the multivariate normal (MVN) distribution. While in certain to sample from these case-specific multivariate distributions in simulation studies (see, e.g.,14 Ghosh
Introduction to Markov Chain Monte Carlo Simulations and their Statistical Analysis
Bernd A. Berg
2004-10-19T23:59:59.000Z
This article is a tutorial on Markov chain Monte Carlo simulations and their statistical analysis. The theoretical concepts are illustrated through many numerical assignments from the author's book on the subject. Computer code (in Fortran) is available for all subjects covered and can be downloaded from the web.
Relative Apparent Synapomorphy Analysis (RASA) I: The Statistical Measurement of Phylogenetic Signal
inference, providing measurable sensitivity and power. The performance of RASA is examined under variousRelative Apparent Synapomorphy Analysis (RASA) I: The Statistical Measurement of PhylogeneticUSDA Forest Service, Reno, Nevada We have developed a new approach to the measurement of phylogenetic signal
AIAA-2003-0867 STATISTICAL ANALYSIS OF INFLOW AND STRUCTURAL RESPONSE
, University of Texas at Austin, Austin, TX 78712 2 Wind Energy Technology Department, Sandia NationalAIAA-2003-0867 STATISTICAL ANALYSIS OF INFLOW AND STRUCTURAL RESPONSE DATA FROM THE LIST PROGRAM Laboratories, Albuquerque, NM 87185 ABSTRACT The Long-Term Inflow and Structural Test (LIST) program
Swan II, J. Edward
Guided Analysis of Hurricane Trends Using Statistical Processes Integrated with Interactive. The system's utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28
Predicting landfalling hurricane numbers from basin hurricane numbers: basic statistical analysis
Laepple, T; Penzer, J; Bellone, E; Nzerem, K; Laepple, Thomas; Jewson, Stephen; Penzer, Jeremy; Bellone, Enrica; Nzerem, Kechi
2007-01-01T23:59:59.000Z
One possible method for predicting landfalling hurricane numbers is to first predict the number of hurricanes in the basin and then convert that prediction to a prediction of landfalling hurricane numbers using an estimated proportion. Should this work better than just predicting landfalling hurricane numbers directly? We perform a basic statistical analysis of this question in the context of a simple abstract model.
Threshold phenomena and complexity: a statistical physics analysis of the random
Duxbury, Phillip M.
Threshold phenomena and complexity: a statistical physics analysis of the random Satis#12;ability problem. R#19;emi Monasson 1 Laboratoire de Physique Th#19;eorique de l'ENS, 75005 Paris. Abstract designed by physicists to deal with optimization or decision problems in an accessible language
Wavelets and statistical analysis of functional magnetic resonance images of the human brain
Breakspear, Michael
Wavelets and statistical analysis of functional magnetic resonance images of the human brain Ed Bullmore Brain Mapping Unit and Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke CNRS UMR 6072, Caen, France, Michael Breakspear Brain Dynamics Centre (Westmead Hospital) and School
Cube Test Analysis of the Statistical Behavior of CubeHash and Skein
International Association for Cryptologic Research (IACR)
Cube Test Analysis of the Statistical Behavior of CubeHash and Skein Alan Kaminsky # May 6, 2010 CubeHash and Skein to try to find nonrandom behavior. Cube tests were used to probe each algorithm test data were calculated on a 40core hybrid SMP cluster parallel computer. The cube test data were
Statistical analysis of electric power production costs JORGE VALENZUELA and MAINAK MAZUMDAR*
Mazumdar, Mainak
whether the utility's own generators should be used to produce power or purchase from outside indeStatistical analysis of electric power production costs JORGE VALENZUELA and MAINAK MAZUMDAR be sucient production at all times to meet the demand for electric power. If a low-cost generating unit fails
Stout, Quentin F.
2008-01-01T23:59:59.000Z
In Computational Statistics and Data Analysis 53 (2008), pp. 289297 Unimodal Regression via Prefix Isotonic Regression Quentin F. Stout University of Michigan Ann Arbor, MI 481092121 Abstract This paper gives algorithms for determining real-valued uni- variate unimodal regressions, that is, for determining
Inoculating Multivariate Schemes Against Differential Attacks
International Association for Cryptologic Research (IACR)
Inoculating Multivariate Schemes Against Differential Attacks Jintai Ding and Jason E. Gower scheme the Perturbed Matsumoto-Imai-Plus (PMI+) cryptosystem. Keywords: multivariate, public key resources. Multivariate public key cryptography provides one alternative since computations in small finite
MULTIVARIATE OUTLYINGNESS FUNCTIONS MAHALANOBIS TYPE OUTLYINGNESS FUNCTIONS
Serfling, Robert
OUTLINE MULTIVARIATE OUTLYINGNESS FUNCTIONS MAHALANOBIS TYPE OUTLYINGNESS FUNCTIONS ADDITION MULTIVARIATE OUTLYINGNESS FUNCTIONS MAHALANOBIS TYPE OUTLYINGNESS FUNCTIONS ADDITION AND REPLACEMENT BREAKDOWN Criterion Robert Serfling NONPARAMETRIC OUTLIER IDENTIFICATION #12;OUTLINE MULTIVARIATE OUTLYINGNESS
The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments
Bihn T. Pham; Jeffrey J. Einerson
2010-06-01T23:59:59.000Z
This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automated processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.
Multivariate extensions of the Golden-Thompson inequality
Frank Hansen
2014-07-02T23:59:59.000Z
We study concave trace functions of several operator variables and formulate and prove multivariate generalisations of the Golden-Thompson inequality. The obtained results imply that certain functionals in quantum statistical mechanics have bounds of the same form as they appear in classical physics.
Stable multivariate Eulerian polynomials and generalized Stirling permutations
Haglund, Jim
Stable multivariate Eulerian polynomials and generalized Stirling permutations J. Haglund, Mirk Abstract We study Eulerian polynomials as the generating polynomials of the descent statistic over Stirling Eulerian polyno- mial for permutations, and extends naturally to r-Stirling and generalized Stirling
Modular multivariable control improves hydrocracking
Chia, T.L.; Lefkowitz, I. [ControlSoft, Inc., Cleveland, OH (United States); Tamas, P.D. [Marathon Oil Co., Robinson, IL (United States)
1996-10-01T23:59:59.000Z
Modular multivariable control (MMC), a system of interconnected, single process variable controllers, can be a user-friendly, reliable and cost-effective alternative to centralized, large-scale multivariable control packages. MMC properties and features derive directly from the properties of the coordinated controller which, in turn, is based on internal model control technology. MMC was applied to a hydrocracking unit involving two process variables and three controller outputs. The paper describes modular multivariable control, MMC properties, tuning considerations, application at the DCS level, constraints handling, and process application and results.
Analysis of Large Scale Structure using Percolation, Genus and Shape Statistics
V. Sahni
1998-03-17T23:59:59.000Z
We probe gravitational clustering in N-body simulations using geometrical descriptors sensitive to `connectedness': the genus curve, percolation and shape statistics. We find that both genus and percolation curves provide complementary probes of large scale structure topology and could be used to discriminate between models of structure formation and the analysis of observational data such as galaxy catalogs and MBR maps. An analysis of `shapes' in N-body simulations has shown that filaments are more pronounced than pancakes. To probe shapes of clusters and superclusters more rigorously we propose a new shape statistic which does not fit isodensity surfaces by ellipsoids (as done earlier). Our shape statistic is derived from fundamental properties of a compact body: its Minkowski functionals. The new shape statistic gives sensible results for topologically simple surfaces such as the ellipsoid, and for more complicated surfaces such as the torus. (Invited talk, to appear in: Proceedings of the IAU Symposium No. 183, Kyoto, Japan Aug. 1997, ed. K. Sato, Kluwer Academic Publ.)
A Complete Statistical Analysis for the Quadrupole Amplitude in an Ellipsoidal Universe
Alessandro Gruppuso
2007-05-17T23:59:59.000Z
A model of Universe with a small eccentricity due to the presence of a magnetic field at the decoupling time (i.e. an Ellipsoidal Universe) has been recently proposed for the solution of the low quadrupole anomaly of the angular power spectrum of cosmic microwave background anisotropies. We present a complete statistical analysis of that model showing that the probability of increasing of the amplitude of the quadrupole is larger than the probability of decreasing in the whole parameters' space.
Statistical Process Variation Analysis of a Graphene FET based LC-VCO for WLAN Applications
Mohanty, Saraju P.
Statistical Process Variation Analysis of a Graphene FET based LC-VCO for WLAN Applications Md Abir.AbirKhan@my.unt.edu, saraju.mohanty@unt.edu, and elias.kougianos@unt.edu Abstract--Graphene which is a single atom layer-frequency electronics due to low Ion/Ioff ratio. In this paper, design exploration of a graphene FET (GFET) based LC
Statistical Analysis of Microgravity Two-Phase Slug Flow via the Drift Flux Model
Larsen, Benjamin A
2014-05-01T23:59:59.000Z
STATISTICAL ANALYSIS OF MICROGRAVITY TWO-PHASE SLUG FLOW VIA THE DRIFT FLUX MODEL A Thesis by BENJAMIN ANDREW LARSEN Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment... made their data available to me and willingly took the time to converse about their work. Finally I would like to thank my parents Donald and Christine Larsen for their love and support in completing my graduate work. v NOMENCLATURE Symbol...
A Statistical Analysis of Santa Barbara Ambulance Response in 2006: Performance Under Load
Chang, Joshua C; Schoenberg, Frederic P.
2009-01-01T23:59:59.000Z
The R Language and Environment for Statistical Computing 17R Development Core Team. R: A Language and Environment for Statistical
HotPatch Web Gateway: Statistical Analysis of Unusual Patches on Protein Surfaces
DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]
Pettit, Frank K.; Bowie, James U. [DOE-Molecular Biology Institute
HotPatch finds unusual patches on the surface of proteins, and computes just how unusual they are (patch rareness), and how likely each patch is to be of functional importance (functional confidence (FC).) The statistical analysis is done by comparing your protein's surface against the surfaces of a large set of proteins whose functional sites are known. Optionally, HotPatch can also write a script that will display the patches on the structure, when the script is loaded into some common molecular visualization programs. HotPatch generates complete statistics (functional confidence and patch rareness) on the most significant patches on your protein. For each property you choose to analyze, you'll receive an email to which will be attached a PDB-format file in which atomic B-factors (temp. factors) are replaced by patch indices; and the PDB file's Header Remarks will give statistical scores and a PDB-format file in which atomic B-factors are replaced by the raw values of the property used for patch analysis (for example, hydrophobicity instead of hydrophobic patches). [Copied with edits from http://hotpatch.mbi.ucla.edu/
Advanced statistical methods for eye movement analysis and modeling: a gentle introduction
Boccignone, Giuseppe
2015-01-01T23:59:59.000Z
In this Chapter we show that by considering eye movements, and in particular, the resulting sequence of gaze shifts, a stochastic process, a wide variety of tools become available for analyses and modelling beyond conventional statistical methods. Such tools encompass random walk analyses and more complex techniques borrowed from the pattern recognition and machine learning fields. After a brief, though critical, probabilistic tour of current computational models of eye movements and visual attention, we lay down the basis for gaze shift pattern analysis. To this end, the concepts of Markov Processes, the Wiener process and related random walks within the Gaussian framework of the Central Limit Theorem will be introduced. Then, we will deliberately violate fundamental assumptions of the Central Limit Theorem to elicit a larger perspective, rooted in statistical physics, for analysing and modelling eye movements in terms of anomalous, non-Gaussian, random walks and modern foraging theory. Eventually, by resort...
Columbia University
The statistical entropy (SE) function has been applied to waste treatment systems to account for dilution solid waste (MSW). A greenhouse gas- forcing factor is also introduced to account for the entropyUse of Statistical Entropy and Life Cycle Analysis to Evaluate Global Warming Potential of Waste
Columbia University
Use of Statistical Entropy and Life Cycle Analysis to Evaluate Global Warming Potential of Waste combusted with energy recovery is introduced to account for additional influxes of carbon when using, however, it is necessary to be able to use statistical entropy to measure the tendency of carbon
Classes of multivariate lifetimes distributions based on the multivariate excess wealth function
Ceragioli, Francesca
Classes of multivariate lifetimes distributions based on the multivariate excess wealth function of their quantile functions and excess wealth func- tions. Here we consider the multivariate versions of these characterizations, defining new multivariate aging classes based on properties of the multivariate u
Statistical Analysis of Mixtures G.R. Carmody February 19, 2008 http://www.cstl.nist.gov/biotech scenarios #12;Statistical Analysis of Mixtures G.R. Carmody February 19, 2008 http://www.cstl.nist.gov/biotech hypotheses #12;Statistical Analysis of Mixtures G.R. Carmody February 19, 2008 http://www.cstl.nist.gov/biotech
Multivariate discriminant and iterated resultant
Jingjun Han
2015-07-22T23:59:59.000Z
In this paper, we study the relationship between iterated resultant and multivariate discriminant. We show that, for generic form $f(X_n)$ with even degree $d$, if the polynomial is squarefreed after each iteration, the multivariate discriminant $\\Delta(f)$ is a factor of the squarefreed iterated resultant. In fact, we find a factor $Hp(f,[x_1,\\ldots,x_n])$ of the squarefreed iterated resultant, and prove that the multivariate discriminant $\\Delta(f)$ is a factor of $Hp(f,[x_1,\\ldots,x_n])$. Moreover, we conjecture that $Hp(f,[x_1,\\ldots,x_n])=\\Delta(f)$ holds for generic form $f$, and show that it is true for generic trivariate form $f(x,y,z)$.
Applications of Minkowski Functionals to the Statistical Analysis of Dark Matter Models
Michael Platzoeder; Thomas Buchert
1995-09-04T23:59:59.000Z
A new method for the statistical analysis of 3D point processes, based on the family of Minkowski functionals, is explained and applied to modelled galaxy distributions generated by a toy-model and cosmological simulations of the large-scale structure in the Universe. These measures are sensitive to both, geometrical and topological properties of spatial patterns and appear to be very effective in discriminating different point processes. Moreover by the means of conditional subsampling, different building blocks of large-scale structures like sheets, filaments and clusters can be detected and extracted from a given distribution.
Walsh, Bruce
35 Multivariate Response: Changes in Covariances The proportional change in the genetic covariances start with development of the multivariate Bulmer's equation for the change in G solely through with an analysis under a general multivariate Gaussian fitness function. This class of fitness functions is very
In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units
Ranjan, Niloo [ORNL; Sanyal, Jibonananda [ORNL; New, Joshua Ryan [ORNL
2013-08-01T23:59:59.000Z
Developing accurate building energy simulation models to assist energy efficiency at speed and scale is one of the research goals of the Whole-Building and Community Integration group, which is a part of Building Technologies Research and Integration Center (BTRIC) at Oak Ridge National Laboratory (ORNL). The aim of the Autotune project is to speed up the automated calibration of building energy models to match measured utility or sensor data. The workflow of this project takes input parameters and runs EnergyPlus simulations on Oak Ridge Leadership Computing Facility s (OLCF) computing resources such as Titan, the world s second fastest supercomputer. Multiple simulations run in parallel on nodes having 16 processors each and a Graphics Processing Unit (GPU). Each node produces a 5.7 GB output file comprising 256 files from 64 simulations. Four types of output data covering monthly, daily, hourly, and 15-minute time steps for each annual simulation is produced. A total of 270TB+ of data has been produced. In this project, the simulation data is statistically analyzed in-situ using GPUs while annual simulations are being computed on the traditional processors. Titan, with its recent addition of 18,688 Compute Unified Device Architecture (CUDA) capable NVIDIA GPUs, has greatly extended its capability for massively parallel data processing. CUDA is used along with C/MPI to calculate statistical metrics such as sum, mean, variance, and standard deviation leveraging GPU acceleration. The workflow developed in this project produces statistical summaries of the data which reduces by multiple orders of magnitude the time and amount of data that needs to be stored. These statistical capabilities are anticipated to be useful for sensitivity analysis of EnergyPlus simulations.
Prediction of Solar Flares from a Statistical Analysis of Events during Solar Cycle 23
Z. Q. Qu
2008-11-14T23:59:59.000Z
Ways to give medium- and short-term predictions of solar flares are proposed according to the statistical analysis of events during solar cycle 23. On one hand, the time distribution of both C and M class flares shows two main periods of 13.2 and 26.4 months in this cycle by wavelet analysis. On the other hand, active regions of specific magnetic configurations and their evolutions give high productivity of C class flares but relatively low productivity of energetic (M and X class) flares. Furthermore, by considering the measurable kinetic features of active regions, i.e., the rotation of the sunspots, some active regions of specified types are observed to have high energetic flare productivity, above 66%. The periodicity of the activity revealed can be used for medium-term C and M class flare forecasting and the high productivity of active regions forms the basis for short-term prediction of individual energetic flares.
Bayesian Calibration of Expensive Multivariate
Oakley, Jeremy
Bayesian Calibration of Expensive Multivariate Computer Experiments Richard D. Wilkinson University of Sheffield This chapter is concered with how to calibrate a computer model to observational data when approach to calibration described here was first given by Kennedy and O'Hagan (2001). Their approach
Jagadheep D. Pandian; Paul F. Goldsmith
2007-08-23T23:59:59.000Z
We present an analysis of the properties of the 6.7 GHz methanol maser sample detected in the Arecibo Methanol Maser Galactic Plane Survey. The distribution of the masers in the Galaxy, and statistics of their multi-wavelength counterparts is consistent with the hypothesis of 6.7 GHz maser emission being associated with massive young stellar objects. Using the detection statistics of our survey, we estimate the minimum number of methanol masers in the Galaxy to be 1275. The l-v diagram of the sample shows the tangent point of the Carina-Sagittarius spiral arm to be around 49.6 degrees, and suggests occurrence of massive star formation along the extension of the Crux-Scutum arm. A Gaussian component analysis of the maser spectra shows the mean line-width to be 0.38 km/s which is more than a factor of two larger than what has been reported in the literature. We also find no evidence that faint methanol masers have different properties than those of their bright counterparts.
Analysis on the Inverse problem Statistical analysis of the inverse problem
regression This is a non-linear regression model. Assumption : we have equal variance measurement errors and trigonometric forms. #12;Analysis on the Inverse problem Introduction Non-linear regression This is a non-linear on the Inverse problem Introduction Linear and non-linear regression Examples : Linear model y = 0 + 1x + 2x2 y
Comparing Methods for Multivariate Nonparametric Regression
Comparing Methods for Multivariate Nonparametric Regression David L. Banks \\Lambda Robert T, of the National Science Foundation or the U.S. government. #12; Keywords: multivariate nonparametric regression, linear regression, stepwise linear regression, additive models, AM, projection pursuit regression, PPR
Majorizing a Multivariate Polynomial Over the Unit Sphere
Leeuw, Jan de
2011-01-01T23:59:59.000Z
R.K.S. Hankin. multipol: multivariate polynomials, 2009. URLMAJORIZING A MULTIVARIATE POLYNOMIAL OVER THE UNIT SPHEREP(x) = ? ? p ? x ? for multivariate polynomials, where ? are
MAJORIZING A MULTIVARIATE POLYNOMIAL OVER THE UNIT SPHERE, WITH APPLICATIONS
De Leeuw, Jan; Groenen, Patrick J.F.
2011-01-01T23:59:59.000Z
R.K.S. Hankin. multipol: multivariate polynomials, 2009. URLMAJORIZING A MULTIVARIATE POLYNOMIAL OVER THE UNIT SPHERE,P (x) = p ? x ? for multivariate polynomials, where ? are
Improved Geothermometry Through Multivariate Reaction Path Modeling...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Improved Geothermometry Through Multivariate Reaction Path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators Improved Geothermometry...
Time varying, multivariate volume data reduction
Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS
2010-01-01T23:59:59.000Z
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the advantage of offering smooth animations, while spatial compression can handle volumes of larger dimensions.
MULTIVARIATE INPUT MODELING WITH JOHNSON DISTRIBUTIONS
MULTIVARIATE INPUT MODELING WITH JOHNSON DISTRIBUTIONS Paul M. Stanfield ABCO Automation, Inc. 6202 University Raleigh, NC 276957906, U.S.A. ABSTRACT This paper introduces a new method for multivari ate is compared to traditional multivariate inputmodeling techniques based on the Johnson translation system. 1
Multivariate Autoregressive Models for Classification of Spontaneous
Anderson, Charles W.
Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During University Fort Collins, CO 80523 Abstract This article explores the use of scalar and multivariate, eigenvalues of a correlation matrix, and the KarhunenÂLoâ??eve transÂ form of the multivariate AR coefficients
Hash-based Multivariate Public Key Cryptosystems
International Association for Cryptologic Research (IACR)
Hash-based Multivariate Public Key Cryptosystems WANG Hou-Zhen and ZHANG Huan-Guo The Key for the traditional multivariate public key cryp- tosystems. For example, the signature scheme SFLASH was broken. at ASIACRYPTO'09. Most multivariate schemes known so far are insecure, except maybe the sigature schemes UOV
On multivariate signatureonly public key cryptosystems
International Association for Cryptologic Research (IACR)
On multivariate signatureonly public key cryptosystems Nicolas T. Courtois 1,2 courtois we argument that the problem has many natural solutions within the framework of the multivariate cryptography. First of all it seems that virtually any noninjective multivariate public key is inherently
Multivariate Receptor Models and Model Uncertainty
Washington at Seattle, University of
Multivariate Receptor Models and Model Uncertainty Eun Sug Park Man-Suk Oh Peter Guttorp NRCSET e c provides the Center's primary funding. #12;Multivariate Receptor Models and Model Uncertainty Eun Sug Park1 composition profiles, and the source contributions is the main interest in multivariate receptor modeling. Due
Technical report: Multivariate generalized S-estimators
Van Aelst, Stefan
Technical report: Multivariate generalized S-estimators Roelant E. a, Van Aelst S. a Croux C. b a-estimators for the multivariate regression model. This class of estimators combines high robustness and high efficiency of residuals. In the special case of a multivariate location model, the generalized S-estimator has
MULTIVARIATE PUBLIC KEY CRYPTOSYSTEMS FROM DIOPHANTINE EQUATIONS
Gao, Shuhong
MULTIVARIATE PUBLIC KEY CRYPTOSYSTEMS FROM DIOPHANTINE EQUATIONS SHUHONG GAO AND RAYMOND HEINDL for multivariate public key cryptosystems, which combines ideas from both triangular and oil-vinegar schemes. We the framework. 1. Introduction 1.1. Multivariate Public Key Cryptography. Public key cryptography plays
Bayesian Multivariate Autoregressive Models with Structured Priors
Roberts, Stephen
Bayesian Multivariate Autoregressive Models with Structured Priors W:D:Penny 1 and S:J:Roberts 2 (1) learning algorithm for param- eter estimation and model order selection in Multivariate Autoregressive (MAR and electro-encephalogram (EEG) data. 2 #12; 1 Introduction The Multivariate Autoregressive (MAR) process
Multivariate Time Series Forecasting in Incomplete Environments
Roberts, Stephen
Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 08-03 Seung of Oxford December 2008 #12;Seung Min Lee and Stephen J. Roberts Technical Report PARG 08-03 Multivariate missing observations and forecasting future values in incomplete multivariate time series data. We study
The Univariate Problem The Multivariate Case
Basu, Saugata
Background The Univariate Problem The Multivariate Case Recent Developments and on-going work;Background The Univariate Problem The Multivariate Case Recent Developments and on-going work Outline 1 Background 2 The Univariate Problem 3 The Multivariate Case 4 Recent Developments and on-going work Saugata
Quantitative Genetics, House Sparrows and a Multivariate
Steinsland, Ingelin
NTNU Quantitative Genetics, House Sparrows and a Multivariate Gaussian Markov Random Field Model Quantitative Genetics, House Sparrows and a Multivariate Gaussian Markov Random Field Model Â p.1/25 #12;NTNU. Quantitative Genetics, House Sparrows and a Multivariate Gaussian Markov Random Field Model Â p.2/25 #12;NTNU
Simulating Multivariate Nonhomogeneous Poisson Processes Using Projections
Henderson, Shane
Simulating Multivariate Nonhomogeneous Poisson Processes Using Projections EVAN A. SALTZMAN RAND Cornell University Established techniques for generating an instance of a multivariate nonhomogeneous. V, No. N, MM 20YY, Pages 115. #12;2 · Evan Saltzman et al. 1. INTRODUCTION A multivariate (or
Temporal Multivariate Networks James Abello1
Kobourov, Stephen G.
Temporal Multivariate Networks James Abello1 , Daniel Archambault2 , Jessie Kennedy3 , Stephen. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also
Zilic, Zeljko
________________________________________________________________________________ A Deterministic Multivariate Interpolation Algorithm for Small Finite Fields Zeljko Zilic, Member, IEEE, and Zvonko G. Vranesic, Member, IEEE Abstract--We present a new multivariate interpolation algorithm over of the multivariate generalized Vandermonde matrix associated with the problem. Relative to the univariate
Frome, EL
2005-09-20T23:59:59.000Z
Environmental exposure measurements are, in general, positive and may be subject to left censoring; i.e,. the measured value is less than a ''detection limit''. In occupational monitoring, strategies for assessing workplace exposures typically focus on the mean exposure level or the probability that any measurement exceeds a limit. Parametric methods used to determine acceptable levels of exposure, are often based on a two parameter lognormal distribution. The mean exposure level, an upper percentile, and the exceedance fraction are used to characterize exposure levels, and confidence limits are used to describe the uncertainty in these estimates. Statistical methods for random samples (without non-detects) from the lognormal distribution are well known for each of these situations. In this report, methods for estimating these quantities based on the maximum likelihood method for randomly left censored lognormal data are described and graphical methods are used to evaluate the lognormal assumption. If the lognormal model is in doubt and an alternative distribution for the exposure profile of a similar exposure group is not available, then nonparametric methods for left censored data are used. The mean exposure level, along with the upper confidence limit, is obtained using the product limit estimate, and the upper confidence limit on an upper percentile (i.e., the upper tolerance limit) is obtained using a nonparametric approach. All of these methods are well known but computational complexity has limited their use in routine data analysis with left censored data. The recent development of the R environment for statistical data analysis and graphics has greatly enhanced the availability of high-quality nonproprietary (open source) software that serves as the basis for implementing the methods in this paper.
Lilly, Jonathan
2012-01-01T23:59:59.000Z
Transactions on Signal Processing, 60 (2), 600612. c 2012 IEEE. Personal use of this material is permitted_policies.html. #12;600 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 60, NO. 2, FEBRUARY 2012 Analysis of Modulated for the recovery of such a signal from potentially noisy observations is proposed, and the time-varying bias
Lee, Ho Young
2000-01-01T23:59:59.000Z
A large volume of multivariate data is available from the manufacturing process. A new statistical approach in the context of signal processing is investigated to diagnose the root causes of unusual variability from the multivariate measurement data...
Stellar Evolution A Statistical Model
van Dyk, David
Stellar Evolution A Statistical Model Statistical Computation Analysis of the Hyades Cluster Statistical Analysis of Stellar Evolution David A. van Dyk1 Steven DeGennaro2 Nathan Stein2 William H Statistical Analysis of Stellar Evolution #12;Stellar Evolution A Statistical Model Statistical Computation
Near-Infrared Detection of Flow Injection Analysis by Acoustooptic Tunable Filter-Based
Reid, Scott A.
pretreatment), and the availability of powerful and effective multivariate statistical methods for data wavelength. As a consequence, the multivariate calibration methods cannot be used to analyze data
Sponsored by Department of Mathematical Sciences Optimization with Multivariate
Dentcheva, Darinka
Sponsored by Department of Mathematical Sciences Optimization with Multivariate Conditional Value based on multiple stochastic performance measures (or criteria). Incorporating such multivariate on extending univariate stochastic dominance rules to the multivariate case. However, enforcing multivariate
Multivariate outlier detection with compositional P. Filzmoser(1)
Filzmoser, Peter
Multivariate outlier detection with compositional data P. Filzmoser(1) , K. Hron(2) (1) Dept, CZECH REPUBLIC Abstract Multivariate outlier detection is usually based on Mahalanobis distances possibilities for the interpretation of the identified multivariate outliers are presented. 1 Multivariate
Wolfrum, E. J.; Sluiter, A. D.
2009-01-01T23:59:59.000Z
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
DWPF Sample Vial Insert Study-Statistical Analysis of DWPF Mock-Up Test Data
Harris, S.P. [Westinghouse Savannah River Company, AIKEN, SC (United States)
1997-09-18T23:59:59.000Z
This report is prepared as part of Technical/QA Task Plan WSRC-RP-97-351 which was issued in response to Technical Task Request HLW/DWPF/TTR-970132 submitted by DWPF. Presented in this report is a statistical analysis of DWPF Mock-up test data for evaluation of two new analytical methods which use insert samples from the existing HydragardTM sampler. The first is a new hydrofluoric acid based method called the Cold Chemical Method (Cold Chem) and the second is a modified fusion method.Either new DWPF analytical method could result in a two to three fold improvement in sample analysis time.Both new methods use the existing HydragardTM sampler to collect a smaller insert sample from the process sampling system. The insert testing methodology applies to the DWPF Slurry Mix Evaporator (SME) and the Melter Feed Tank (MFT) samples.The insert sample is named after the initial trials which placed the container inside the sample (peanut) vials. Samples in small 3 ml containers (Inserts) are analyzed by either the cold chemical method or a modified fusion method. The current analytical method uses a HydragardTM sample station to obtain nearly full 15 ml peanut vials. The samples are prepared by a multi-step process for Inductively Coupled Plasma (ICP) analysis by drying, vitrification, grinding and finally dissolution by either mixed acid or fusion. In contrast, the insert sample is placed directly in the dissolution vessel, thus eliminating the drying, vitrification and grinding operations for the Cold chem method. Although the modified fusion still requires drying and calcine conversion, the process is rapid due to the decreased sample size and that no vitrification step is required.A slurry feed simulant material was acquired from the TNX pilot facility from the test run designated as PX-7.The Mock-up test data were gathered on the basis of a statistical design presented in SRT-SCS-97004 (Rev. 0). Simulant PX-7 samples were taken in the DWPF Analytical Cell Mock-up Facility using 3 ml inserts and 15 ml peanut vials. A number of the insert samples were analyzed by Cold Chem and compared with full peanut vial samples analyzed by the current methods. The remaining inserts were analyzed by the modified fusion method, for comparison to the current method, and also to obtain a calcine correction factor. The simulant was within 40 - 42 wt% solids in order to provide a rheology within the DWPF design range. The rheology at 42 wt% was approximately 47 dynes/cm2 yield stress at 251/4C.
A statistical analysis of avalanching heat transport in stationary enhanced core confinement regimes
Tokunaga, S.; Jhang, Hogun; Kim, S. S. [WCI Center for Fusion Theory, National Fusion Research Institute, 52, Yeoeun-dong, Yusung-Gu, Daejon (Korea, Republic of); Diamond, P. H. [WCI Center for Fusion Theory, National Fusion Research Institute, 52, Yeoeun-dong, Yusung-Gu, Daejon (Korea, Republic of); Center for Astrophysics and Space Sciences and Department of Physics, University of California San Diego, La Jolla, California 92093-0429 (United States)
2012-09-15T23:59:59.000Z
We present a statistical analysis of heat transport in stationary enhanced confinement regimes obtained from flux-driven gyrofluid simulations. The probability density functions of heat flux in improved confinement regimes, characterized by the Nusselt number, show significant deviation from Gaussian, with a markedly fat tail, implying the existence of heat avalanches. Two types of avalanching transport are found to be relevant to stationary states, depending on the degree of turbulence suppression. In the weakly suppressed regime, heat avalanches occur in the form of quasi-periodic (QP) heat pulses. Collisional relaxation of zonal flow is likely to be the origin of these QP heat pulses. This phenomenon is similar to transient limit cycle oscillations observed prior to edge pedestal formation in recent experiments. On the other hand, a spectral analysis of heat flux in the strongly suppressed regime shows the emergence of a 1/f (f is the frequency) band, suggesting the presence of self-organized criticality (SOC)-like episodic heat avalanches. This episodic 1/f heat avalanches have a long temporal correlation and constitute the dominant transport process in this regime.
Stellar Evolution A Statistical Model
van Dyk, David
Stellar Evolution A Statistical Model Statistical Computation Analysis of the Hyades Cluster Embedding Computer Models for Stellar Evolution into a Coherent Statistical Analysis David A. van Dyk1 Analysis of Stellar Evolution #12;Stellar Evolution A Statistical Model Statistical Computation Analysis
A Multivariate Statistical Approach to Spatial Representation of
Vermont, University of
for utilizing qualitative and quantitative information (specifically, multiple water quality measurements to detect and monitor contaminants. Introduction The cost of waste disposal does not end mechanism of municipal solid waste disposal in most developed nations, with the U.S. and Europe placing over
Neurogenomics in the mouse model : multivariate statistical methods and analyses
Zapala, Matthew Alan
2007-01-01T23:59:59.000Z
development of the rat hypothalamus. Adv Anat Embryol Cellmidbrain, excluding hypothalamus (DiE-MD), entorhinalformation (HiF), hypothalamus (Hy), inferior colliculus (
Varvarigo, Emmanouel "Manos"
Delay Components of Job Processing in a Grid: Statistical Analysis and Modeling K}@ceid.upatras.gr Abstract The existence of good probabilistic models for the job arrival process and the delay components introduced at the different stages of job processing in a Grid environment is important for the improved
Statistical analysis of Multi-Material Components using Dual Energy CT Christoph Heinzl, Johann plastics-metal components. The presented work makes use of dual energy CT data acquisi- tion for artefact pipeline based on the dual ex- posure technique of dual energy CT. After prefilter- ing and multi
Saldin, Dilano
Actuarial science is the quantitative analysis of risk. In addition to mathematics and statistics. Actuaries help individuals, businesses and society manage risk by evaluating the likelihood of future events's risk tolerance with various risk parameters such as age of the insured, health status, place
Yu, Victoria; Kishan, Amar U.; Cao, Minsong; Low, Daniel; Lee, Percy; Ruan, Dan, E-mail: druan@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90024 (United States)] [Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90024 (United States)
2014-03-15T23:59:59.000Z
Purpose: To demonstrate a new method of evaluating dose response of treatment-induced lung radiographic injury post-SBRT (stereotactic body radiotherapy) treatment and the discovery of bimodal dose behavior within clinically identified injury volumes. Methods: Follow-up CT scans at 3, 6, and 12 months were acquired from 24 patients treated with SBRT for stage-1 primary lung cancers or oligometastic lesions. Injury regions in these scans were propagated to the planning CT coordinates by performing deformable registration of the follow-ups to the planning CTs. A bimodal behavior was repeatedly observed from the probability distribution for dose values within the deformed injury regions. Based on a mixture-Gaussian assumption, an Expectation-Maximization (EM) algorithm was used to obtain characteristic parameters for such distribution. Geometric analysis was performed to interpret such parameters and infer the critical dose level that is potentially inductive of post-SBRT lung injury. Results: The Gaussian mixture obtained from the EM algorithm closely approximates the empirical dose histogram within the injury volume with good consistency. The average Kullback-Leibler divergence values between the empirical differential dose volume histogram and the EM-obtained Gaussian mixture distribution were calculated to be 0.069, 0.063, and 0.092 for the 3, 6, and 12 month follow-up groups, respectively. The lower Gaussian component was located at approximately 70% prescription dose (35 Gy) for all three follow-up time points. The higher Gaussian component, contributed by the dose received by planning target volume, was located at around 107% of the prescription dose. Geometrical analysis suggests the mean of the lower Gaussian component, located at 35 Gy, as a possible indicator for a critical dose that induces lung injury after SBRT. Conclusions: An innovative and improved method for analyzing the correspondence between lung radiographic injury and SBRT treatment dose has been demonstrated. Bimodal behavior was observed in the dose distribution of lung injury after SBRT. Novel statistical and geometrical analysis has shown that the systematically quantified low-dose peak at approximately 35 Gy, or 70% prescription dose, is a good indication of a critical dose for injury. The determined critical dose of 35 Gy resembles the critical dose volume limit of 30 Gy for ipsilateral bronchus in RTOG 0618 and results from previous studies. The authors seek to further extend this improved analysis method to a larger cohort to better understand the interpatient variation in radiographic lung injury dose response post-SBRT.
Of Disasters and Dragon Kings: A Statistical Analysis of Nuclear Power Incidents & Accidents
Wheatley, Spencer; Sornette, Didier
2015-01-01T23:59:59.000Z
We provide, and perform a risk theoretic statistical analysis of, a dataset that is 75 percent larger than the previous best dataset on nuclear incidents and accidents, comparing three measures of severity: INES (International Nuclear Event Scale), radiation released, and damage dollar losses. The annual rate of nuclear accidents, with size above 20 Million US$, per plant, decreased from the 1950s until dropping significantly after Chernobyl (April, 1986). The rate is now roughly stable at 0.002 to 0.003, i.e., around 1 event per year across the current fleet. The distribution of damage values changed after Three Mile Island (TMI; March, 1979), where moderate damages were suppressed but the tail became very heavy, being described by a Pareto distribution with tail index 0.55. Further, there is a runaway disaster regime, associated with the "dragon-king" phenomenon, amplifying the risk of extreme damage. In fact, the damage of the largest event (Fukushima; March, 2011) is equal to 60 percent of the total damag...
Statistical Analysis of Baseline Load Models for Non-Residential Buildings
Coughlin, Katie; Piette, Mary Ann; Goldman, Charles; Kiliccote, Sila
2008-11-10T23:59:59.000Z
Policymakers are encouraging the development of standardized and consistent methods to quantify the electric load impacts of demand response programs. For load impacts, an essential part of the analysis is the estimation of the baseline load profile. In this paper, we present a statistical evaluation of the performance of several different models used to calculate baselines for commercial buildings participating in a demand response program in California. In our approach, we use the model to estimate baseline loads for a large set of proxy event days for which the actual load data are also available. Measures of the accuracy and bias of different models, the importance of weather effects, and the effect of applying morning adjustment factors (which use data from the day of the event to adjust the estimated baseline) are presented. Our results suggest that (1) the accuracy of baseline load models can be improved substantially by applying a morning adjustment, (2) the characterization of building loads by variability and weather sensitivity is a useful indicator of which types of baseline models will perform well, and (3) models that incorporate temperature either improve the accuracy of the model fit or do not change it.
Statistical Analysis of Microarray Data with Replicated Spots: A Case Study withSynechococcusWH8102
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; Haaland, D. M.; Timlin, J. A.; Elbourne, L. D. H.; Palenik, B.; Paulsen, I. T.
2009-01-01T23:59:59.000Z
Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in partmore »to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.« less
Spectral mixture analysis of EELS spectrum-images Nicolas Dobigeon a
Tourneret, Jean-Yves
2012 Available online 1 June 2012 Keywords: Electron energy-loss spectroscopy (EELS) Spectrum imaging Multivariate statistical analysis Spectral mixture analysis a b s t r a c t Recent advances in detectors energy-loss spectroscopy (EELS). SU generally over- comes standard limitations inherent to other
composition and exposure patterns will likely alter sample design and analysis needs for other forest types. 1996. Statistical Considerations for Plot Design, Sampling Procedures, Analysis... Arbaugh and Bednar29USDA Forest Service Gen. Tech. Rep. PSW-GTR-155. 1996. Statistical Considerations for Plot Design
Comparing Methods for Multivariate Nonparametric Regression
Comparing Methods for Multivariate Nonparametric Regression David L. Banks Robert T. Olszewskiy Roy, of the National Science Foundation or the U.S. government. #12;Keywords: multivariate nonparametric regression, linear regression, stepwise linear regression, additive models, AM, projection pursuit regression, PPR
Reddy, T. A.; Claridge, D.; Wu, J.
1992-01-01T23:59:59.000Z
Statistical models of energy use in commercial buildings are being increasingly used not only for predicting retrofit savings but also for identifying improper operation of HVAC systems. The conventional approach involves using multiple regression...
Multivariate orthogonal polynomial and integrable systems
Gerardo Ariznabarreta; Manuel Mañas
2015-03-05T23:59:59.000Z
Multivariate orthogonal polynomials in $D$ real dimensions are considered from the perspective of the Cholesky factorization of a moment matrix. The approach allows for the construction of corresponding multivariate orthogonal polynomials, associated second kind functions, Jacobi type matrices and associated three term relations and also Christoffel-Darboux formul{\\ae}. The multivariate orthogonal polynomials, its second kind functions and the corresponding Christoffel-Darboux kernels are shown to be quasi-determinants --as well as Schur complements-- of bordered truncations of the moment matrix; quasi-tau functions are introduced. It is proven that the second kind functions are multivariate Cauchy transforms of the multivariate orthogonal polynomials. Discrete and continuous deformations of the measure lead to Toda type integrable hierarchy, being the corresponding flows described through Lax and Zakharov-Shabat equations; bilinear equations are found. Varying size matrix nonlinear partial difference and differential equations of the 2D Toda lattice type are shown to be solved by matrix coefficients of the multivariate orthogonal polynomials. The discrete flows lead to expressions for the multivariate orthogonal polynomials and its second kind functions in terms of shifted quasi-tau matrices, which generalize to the multidimensional realm those that relate the Baker and adjoint Baker functions with ratios of Miwa shifted $\\tau$-functions in the 1D scenario. In this context, the multivariate extension of the elementary Darboux transformation is given in terms of quasi-determinants of matrices built up by the evaluation, at a poised set of nodes lying in an appropriate hyperplane in $\\mathbb R^D$, of the multivariate orthogonal polynomials. The multivariate Christoffel formula for the iteration of $m$ elementary Darboux transformations is given as a quasi-determinant.
A multivariate quadrature based moment method for supersonic combustion modeling
Raman, Venkat
A multivariate quadrature based moment method for supersonic combustion modeling Pratik Donde- ture method of moments (DQMOM) is well suited for multivariate problems like combustion. Numerical is developed. A decoupling procedure allows extension of this method to multivariate problems. Se
MULTIVARIATE VISUALIZATION OF DATA QUALITY ELEMENTS FOR COASTAL ZONE MONITORING
MULTIVARIATE VISUALIZATION OF DATA QUALITY ELEMENTS FOR COASTAL ZONE MONITORING D. E. van de Vlag for illustrating quantitative values of quality elements using multivariate visualization techniques. Quality, temporal accuracy and completeness. By combining multivariate visualization with the technique of multiple
Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease
Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease Christian Habeck January 2008 Available online 14 February 2008 We performed univariate and multivariate discriminant univariate and multivariate analyses produced markers with high classification accuracy in the derivation
Nonparametric Multivariate Descriptive Measures Based on Spatial Quantiles
Serfling, Robert
Nonparametric Multivariate Descriptive Measures Based on Spatial Quantiles Robert Serfling1. Here we consider the multivariate context and utilize the "spatial quantiles", a recent vector introduce and study nonparametric measures of multivariate location, spread, skewness and kurtosis
Agglomerative Multivariate Information Noam Slonim Nir Friedman Naftali Tishby
Friedman, Nir
Agglomerative Multivariate Information Bottleneck Noam Slonim Nir Friedman Naftali Tishby School princi- pled framework for multivariate extensions of the information bottleneck method that allows us] introduce multivariate extension of the IB principle. This extension allows us to consider cases where
Distributed Multivariate Regression Using Wavelet-based Collective Data Mining.
Kargupta, Hilol
Distributed Multivariate Regression Using Wavelet-based Collective Data Mining. Daryl E a method for distributed multivariate regression using wavelet- based Collective Data Mining (CDM employed in parametric multivariate regression to provide an effective data mining technique for use
Context-free Grammars and Multivariate Stable Polynomials over Stirling Permutations
Chen, Bill
Context-free Grammars and Multivariate Stable Polynomials over Stirling Permutations William Y of some results of BÂ´ona, Brenti, Janson, Kuba, and Panholzer concerning Stirling permutations. Let Bn(x) be the generating polynomials of the descent statistic over Legendre-Stirling permutations, and let Tn(x) = 2n Cn
ERROR MODELS FOR LIGHT SENSORS BY STATISTICAL ANALYSIS OF RAW SENSOR MEASUREMENTS
Potkonjak, Miodrag
silicon solar cell that converts light impulses directly into electrical charges that can easily-based systems including calibration, sensor fusion and power management. We developed a system of statistical the standard procedure is to use error models to enable calibration, in a variant of our approach, we use
Statistical Analysis of High-Cycle Fatigue Behavior of Friction Stir Welded AA5083-H321
Grujicic, Mica
-hardened/stabilized Al-Mg-Mn alloy) are characterized by a relatively large statistical scatter. This scatter is closely process is particularly suited for butt and lap joining of aluminum alloys which are otherwise quite such as shipbuilding/marine, aerospace, railway, land transportation, etc. The basic concept behind the FSW process
Widen, Joakim; Waeckelgaard, Ewa [Department of Engineering Sciences, The Aangstroem Laboratory, Uppsala University, P.O. Box 534, SE-751 21 Uppsala (Sweden); Paatero, Jukka; Lund, Peter [Advanced Energy Systems, Helsinki University of Technology, P.O. Box 2200, FI-02015 HUT (Finland)
2010-03-15T23:59:59.000Z
The trend of increasing application of distributed generation with solar photovoltaics (PV-DG) suggests that a widespread integration in existing low-voltage (LV) grids is possible in the future. With massive integration in LV grids, a major concern is the possible negative impacts of excess power injection from on-site generation. For power-flow simulations of such grid impacts, an important consideration is the time resolution of demand and generation data. This paper investigates the impact of time averaging on high-resolution data series of domestic electricity demand and PV-DG output and on voltages in a simulated LV grid. Effects of 10-minutely and hourly averaging on descriptive statistics and duration curves were determined. Although time averaging has a considerable impact on statistical properties of the demand in individual households, the impact is smaller on aggregate demand, already smoothed from random coincidence, and on PV-DG output. Consequently, the statistical distribution of simulated grid voltages was also robust against time averaging. The overall judgement is that statistical investigation of voltage variations in the presence of PV-DG does not require higher resolution than hourly. (author)
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM MATTHEW STEPHENS Departments of Statistics and Human statistical methods in genomics, among other areas of application. A typical workflow consists of i
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM HUIBIN ZHOU Department of Statistics Yale University to obtain statistical inference for estimation of Gaussian Graphical Model? A regression approach
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM JEROME FRIEDMAN Department of Statistics Stanford Ensemble methods have emerged as being among the most powerful statistical learning techniques. It is shown
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM SAMUEL KOU Department of Statistics Harvard University Stochastic Inference of Dynamic System Models: From Single-Molecule Experiments to Statistical
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM GOURAB MUKHERJEE Department of Statistics Stanford directions in statistical probability forecasting. Building on these parallels we present a frequentist
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM KAMIAR RAD Department of Statistics Columbia carrying a finite total amount of information, the asymptotic sufficient statistics of the stimulus
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM ADRIAN RAFTERY Department of Statistics University will describe a Bayesian statistical method for probabilistic population projections for all countries
A diagnostic procedure for multivariate quality control
Keserla, Adhinarayan A.
1993-01-01T23:59:59.000Z
of the correlations among the variables by monitoring all the variables simultaneously using a single control procedure rather than monitoring the variables on an individual basis. Multivariate control procedures are commonly used for monitoring such process...
SU-E-J-261: Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix
Michalski, D; Huq, M; Bednarz, G; Lalonde, R; Yang, Y; Heron, D [University of Pittsburgh Medical Center, Pittsburgh, PA (United States)
2014-06-01T23:59:59.000Z
Purpose: To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover. Methods: 20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared. Results: Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same is for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans. Conclusion: Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied. Funding provided by Varian Medical Systems via Investigator Initiated Research Project.
Statistical Analysis of LifeData with Masked CauseofFailure
Basu, Sanjib
, a detailed Failure Mode Effect Analysis (FMEA) can be carried out in a routine manner. In reliability been pursued under the general heading of Failure Mode Effect Analysis (FMEA) when the exact causes
Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification
Sarri, A; Dias, F
2012-01-01T23:59:59.000Z
Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake). Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the Outer Product Emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the Leave-One-O...
, for example, working memory operations involving the temporary maintenance and manipula- tion of informationA new statistical method for testing hypotheses of neuropsychological/MRI relationships applied partial least squares (PLS) as a novel multivariate statistical technique to examine
Extracting bb Higgs Decay Signals using Multivariate Techniques
Smith, W Clarke; /George Washington U. /SLAC
2012-08-28T23:59:59.000Z
For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.
Development of Statistical Energy Analysis Tools for Toyota Motor Engineering & Manufacturing
Chen, J; Collins, Ro.; Gao, G.; Schaffer, D.; Wu, J.
2014-01-01T23:59:59.000Z
level • Body Weld – little statistical difference between plants ? Fixed • Plant A best overall • Plant D and Plant E generally worse ESL-IE-14-05-06 Proceedings of the Thrity-Sixth Industrial Energy Technology Conference New Orleans, LA. May 20-23, 2014... 20-23, 2014 Shop Efficiency Rankings - Variable Electricity Paint Assembly Body Weld Plastic Stamp 1 Plant A Plant D Plant C Plant C Plant C 2 Plant E Plant E Plant D Plant A Plant E 3 Plant C Plant A Plant F Plant E Plant A 4 Plant D Plant C Plant A...
Development of Statistical Energy Analysis Tools for Toyota Motor Engineering & Manufacturing
Chen, J; Collins, Ro.; Gao, G.; Schaffer, D.; Wu, J.
2014-01-01T23:59:59.000Z
level • Body Weld – little statistical difference between plants ? Fixed • Plant A best overall • Plant D and Plant E generally worse ESL-IE-14-05-06 Proceedings of the Thrity-Sixth Industrial Energy Technology Conference New Orleans, LA. May 20-23, 2014... 20-23, 2014 Shop Efficiency Rankings - Variable Electricity Paint Assembly Body Weld Plastic Stamp 1 Plant A Plant D Plant C Plant C Plant C 2 Plant E Plant E Plant D Plant A Plant E 3 Plant C Plant A Plant F Plant E Plant A 4 Plant D Plant C Plant A...
Guo, Genliang; George, S.A.; Lindsey, R.P.
1997-08-01T23:59:59.000Z
Thirty-six sets of surface lineaments and fractures mapped from satellite images and/or aerial photos from parts of the Mid-continent and Colorado Plateau regions were collected, digitized, and statistically analyzed in order to obtain the probability distribution functions of natural fractures for characterizing naturally fractured reservoirs. The orientations and lengths of the surface linear features were calculated using the digitized coordinates of the two end points of each individual linear feature. The spacing data of the surface linear features within an individual set were, obtained using a new analytical sampling technique. Statistical analyses were then performed to find the best-fit probability distribution functions for the orientation, length, and spacing of each data set. Twenty-five hypothesized probability distribution functions were used to fit each data set. A chi-square goodness-of-fit test was used to rank the significance of each fit. A distribution which provides the lowest chi-square goodness-of-fit value was considered the best-fit distribution. The orientations of surface linear features were best-fitted by triangular, normal, or logistic distributions; the lengths were best-fitted by PearsonVI, PearsonV, lognormal2, or extreme-value distributions; and the spacing data were best-fitted by lognormal2, PearsonVI, or lognormal distributions. These probability functions can be used to stochastically characterize naturally fractured reservoirs.
Applications of Universal Source Coding to Statistical Analysis of Time Series
Ryabko, Boris
2008-01-01T23:59:59.000Z
We show how universal codes can be used for solving some of the most important statistical problems for time series. By definition, a universal code (or a universal lossless data compressor) can compress any sequence generated by a stationary and ergodic source asymptotically to the Shannon entropy, which, in turn, is the best achievable ratio for lossless data compressors. We consider finite-alphabet and real-valued time series and the following problems: estimation of the limiting probabilities for finite-alphabet time series and estimation of the density for real-valued time series, the on-line prediction, regression, classification (or problems with side information) for both types of the time series and the following problems of hypothesis testing: goodness-of-fit testing, or identity testing, and testing of serial independence. It is important to note that all problems are considered in the framework of classical mathematical statistics and, on the other hand, everyday methods of data compression (or ar...
Crow, Ben D
2006-01-01T23:59:59.000Z
of Globalization: Statistics Weiss, L. (1997). "of Globalization: Statistics Milanovic, B. (1999). Truethe focus of global statistics, particularly in relation to
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
LeeAnn Martinez (505) 667-3308 Email Find Expertise header Search our employee skills database Statistical Sciences Statistical Sciences provides statistical reasoning and...
ibr: Iterative bias reduction multivariate smoothing
Hengartner, Nicholas W [Los Alamos National Laboratory; Cornillon, Pierre-andre [AGRO-SUP, FRANCE; Matzner - Lober, Eric [RENNES 2, FRANCE
2009-01-01T23:59:59.000Z
Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.
Buechler, Steven
Cancer Prognosis Through Gene Expression Analysis Steven Buechler, Applied the breast cancer patients who can avoid chemotherapy without increasing the risk of recurrence. Background. Following the initial surgery, many breast cancer patients
The Speedup-Test: A Statistical Methodology for Program Speedup Analysis and Computation
Paris-Sud XI, Université de
TOUATI , Julien WORMS, S´ebastien BRIAIS May 2012 Abstract In the area of high performance computing improvement compared to the usual performance analysis method in high performance computing. We explain
Statistical static timing analysis considering the impact of power supply noise in VLSI circuits
Kim, Hyun Sung
2009-06-02T23:59:59.000Z
As semiconductor technology is scaled and voltage level is reduced, the impact of the variation in power supply has become very significant in predicting the realistic worst-case delays in integrated circuits. The analysis of power supply noise...
Rebound 2007: Analysis of U.S. Light-Duty Vehicle Travel Statistics
Greene, David L [ORNL
2010-01-01T23:59:59.000Z
U.S. national time series data on vehicle travel by passenger cars and light trucks covering the period 1966 2007 are used to test for the existence, size and stability of the rebound effect for motor vehicle fuel efficiency on vehicle travel. The data show a statistically significant effect of gasoline price on vehicle travel but do not support the existence of a direct impact of fuel efficiency on vehicle travel. Additional tests indicate that fuel price effects have not been constant over time, although the hypothesis of symmetry with respect to price increases and decreases is not rejected. Small and Van Dender (2007) model of a declining rebound effect with income is tested and similar results are obtained.
He, Y -H; Zhang, W -J; Zhang, L; Wu, J -J; Chen, S -J; You, L -X; Wang, Z
2015-01-01T23:59:59.000Z
Counting rate is a key parameter of superconducting nanowire single photon detectors (SNSPD) and is determined by the current recovery time of an SNSPD after a detection event. We propose a new method to study the transient detection efficiency (DE) and pulse amplitude during the current recovery process by statistically analyzing the single photon response of an SNSPD under photon illumination with a high repetition rate. The transient DE results match well with the DEs deduced from the static current dependence of DE combined with the waveform of a single-photon detection event. This proves that the static measurement results can be used to analyze the transient current recovery process after a detection event. The results are relevant for understanding the current recovery process of SNSPDs after a detection event and for determining the counting rate of SNSPDs.
Statistica Sinica ??(200?), 000-000 1 A MULTIVARIATE GAUSSIAN PROCESS FACTOR MODEL
Kass, Rob
kinematic dataset. Key words and phrases: Multivariate Gaussian Process, Dynamical Factor Analysis it contains over 20 degrees of freedom, mechanical constraints and, plausibly, complex and non-linear inter principal components analysis (PCA), can go far in this direction but do not conform to the repeated
Dr. Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson
2012-10-01T23:59:59.000Z
As part of the Research and Development program for Next Generation High Temperature Reactors (HTR), a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. The data representing the crucial test fuel conditions (e.g., temperature, neutron fast fluence, and burnup) while impossible to obtain from direct measurements are calculated by physics and thermal models. The irradiation and post-irradiation examination (PIE) experimental data are used in model calibration effort to reduce the inherent uncertainty of simulation results. This paper is focused on fuel temperature predicted by the ABAQUS code’s finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for improving qualification of AGR-1 thermocouple data. The present work exercises the idea that the abnormal trends of measured data observed from statistical analysis may be caused by either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. As an example, the uneven reduction of the control gas gap in Capsule 5 revealed by the capsule metrology measurements in PIE helps justify the reduction in TC readings instead of TC drift. This in turn prompts modification of thermal model to better fit with experimental data, thus help increase confidence, and in other word reduce model uncertainties in thermal simulation results of the AGR-1 test.
Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson
2014-05-01T23:59:59.000Z
As part of the High Temperature Reactors (HTR) R&D program, a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. While not possible to obtain by direct measurements in the tests, crucial fuel conditions (e.g., temperature, neutron fast fluence, and burnup) are calculated using core physics and thermal modeling codes. This paper is focused on AGR test fuel temperature predicted by the ABAQUS code's finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for qualification of AGR-1 thermocouple data. Abnormal trends in measured data revealed by the statistical analysis are traced to either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. The main thrust of this work is to exploit the variety of data obtained in irradiation and post-irradiation examination (PIE) for assessment of modeling assumptions. As an example, the uneven reduction of the control gas gap in Capsule 5 found in the capsule metrology measurements in PIE helps identify mechanisms other than TC drift causing the decrease in TC readings. This suggests a more physics-based modification of the thermal model that leads to a better fit with experimental data, thus reducing model uncertainty and increasing confidence in the calculated fuel temperatures of the AGR-1 test.
Statistical static timing analysis considering the impact of power supply noise in VLSI circuits
Kim, Hyun Sung
2009-06-02T23:59:59.000Z
less random than between the gates within a module. 32 REFERENCES [1] Y. M. Jiang and K. T. Cheng, ?Analysis of Performance Impact Caused by Power Supply Noise in Deep Submicron Devices,? ACM/IEEE Design Automation Conf., New... Orleans, LA, June 1999, pp. 760-765. [2] S. Pant, D. Blaauw, V. Zolotov, S. Sundareswaran and R. Panda, ?Vectorless Analysis of Supply Noise Induced Delay Variation,? IEEE/ACM Int?l Conf. Computer Aided Design, San Jose, CA, Nov. 2003, pp. 184-191. [3...
Osses, Axel
statistical information tools for the analysis of air quality monitoring networks By A. OSSES1,2 , L. GALLARDO that information tools as those presented here should be used in a complementary way when addressing the analysis of an air quality network for planning and evaluation purposes. 1 Introduction The objectives
Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining
Strelcov, Evgheni [ORNL; Belianinov, Alex [ORNL; Hsieh, Ying-Hui [National Chiao Tung University, Hsinchu, Taiwan; Jesse, Stephen [ORNL; Baddorf, Arthur P [ORNL; Chu, Ying Hao [National Chiao Tung University, Hsinchu, Taiwan; Kalinin, Sergei V [ORNL
2014-01-01T23:59:59.000Z
Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. K-means and Bayesian demixing analysis suggests the characteristic response be separated into four components, with hysteretic type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.
Statistical analysis of the overnight and daytime return Fengzhong Wang,1
Stanley, H. Eugene
are of great importance for economics and econophysics research 121 . A key topic of the market studies of the financial markets. Practically, this study can help traders to improve trading strategies at the market open, there is still lack of a comprehen- sive analysis of the overnight and daytime price change for a leading market
Cafarella, Michael J.
and control flow analysis methods form the basis of many programmer productivity tools. However, these methods--such as plagiarism detec- tion and bug finding--rely on knowing a piece of code's relative semantic importance previously unknown bugs in a collection of real deployed programs. 1. Introduction Standard data flow
Cafarella, Michael J.
programmer productivity tools. However, these methods have been largely limited to analyzing one program--such as plagiarism detec- tion and bug finding--rely on knowing a piece of code's relative semantic importance.1145/2660193.2660226 1. Introduction Standard data flow and control flow analysis methods form the basis of many
Statistical Laboratory & Department of Statistics
Statistical Laboratory & Department of Statistics Annual Report July 1, 2005 to December 31, 2006...............................................33 Statistical Computing Section ......................................34 CSSM and statistical methodology in the nutritional sciences. We were also very pleased to secure a permanent lecturer
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM Joint seminar with Stevanovich Center PHILIPPE RIGOLLET Operations Research and Financial Engineering, Princeton University The Statistical Price to Pay ABSTRACT Computational limitations of statistical problems have largely been ignored or simply over- come
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Series TONG ZHANG Department of Statistics Rutgers University High Dimensional Statistical Analysis for Complex Sparse Estimation Problems the seminar in Eckhart 110. ABSTRACT This talk presents theoretical results for high dimensional statistical
Statistical Analysis of Historical State-Level Residential Energy Consumption Trends
Belzer, David B.; Cort, Katherine A.
2004-08-01T23:59:59.000Z
Developing an accurate picture of the major trends in energy consumption in the nation’s stock of residential buildings can serve a variety of national and regional program planning and policy needs related to energy use. This paper employs regression analysis and uses the PRISM (Princeton Scorekeeping Method) approach with historical data to provide some insight into overall changes in the thermal integrity of the residential building stock by state. Although national energy use intensity estimates exist in aggregate, these numbers shed little light on what drives building consumption, as opposing influences are hidden within the measurement (e.g., more appliances that increase energy use while shell improvements reduce it). This study addresses this issue by estimating changes in the reference temperatures that best characterize the existing residential building stock on a state basis. Improvements in building thermal integrity are reflected by declines in the heating reference temperature, holding other factors constant. Heating and cooling-day estimates to various reference temperatures were computed from monthly average temperature data for approximately 350 climatic divisions in the U.S. A simple cross-sectional analysis is employed to try to explain the differential impacts across states. Among other factors, this analysis considers the impact that the relative growth in the number of residential buildings and the stringency of building energy codes has had on residential building energy use. This paper describes the methodology used, presents results, and suggests directions for future research.
32. Statistics 1 32. STATISTICS
Masci, Frank
an overview of statistical methods used in High Energy Physics. In statistics, we are interested in using and their statistical uncertainties in High Energy Physics. In Bayesian statistics, the interpretation of probability32. Statistics 1 32. STATISTICS Revised September 2007 by G. Cowan (RHUL). This chapter gives
Statistical analysis of liquid seepage in partially saturated heterogeneous fracture systems
Liou, T.S.
1999-12-01T23:59:59.000Z
Field evidence suggests that water flow in unsaturated fracture systems may occur along fast preferential flow paths. However, conventional macroscale continuum approaches generally predict the downward migration of water as a spatially uniform wetting front subjected to strong inhibition into the partially saturated rock matrix. One possible cause of this discrepancy may be the spatially random geometry of the fracture surfaces, and hence, the irregular fracture aperture. Therefore, a numerical model was developed in this study to investigate the effects of geometric features of natural rock fractures on liquid seepage and solute transport in 2-D planar fractures under isothermal, partially saturated conditions. The fractures were conceptualized as 2-D heterogeneous porous media that are characterized by their spatially correlated permeability fields. A statistical simulator, which uses a simulated annealing (SA) algorithm, was employed to generate synthetic permeability fields. Hypothesized geometric features that are expected to be relevant for seepage behavior, such as spatially correlated asperity contacts, were considered in the SA algorithm. Most importantly, a new perturbation mechanism for SA was developed in order to consider specifically the spatial correlation near conditioning asperity contacts. Numerical simulations of fluid flow and solute transport were then performed in these synthetic fractures by the flow simulator TOUGH2, assuming that the effects of matrix permeability, gas phase pressure, capillary/permeability hysteresis, and molecular diffusion can be neglected. Results of flow simulation showed that liquid seepage in partially saturated fractures is characterized by localized preferential flow, along with bypassing, funneling, and localized ponding. Seepage pattern is dominated by the fraction of asperity contracts, and their shape, size, and spatial correlation. However, the correlation structure of permeability field is less important than the spatial correlation of asperity contacts. A faster breakthrough was observed in fractures subjected to higher normal stress, accompanied with a nonlinearly decreasing trend of the effective permeability. Interestingly, seepage dispersion is generally higher in fractures with intermediate fraction of asperity contacts; but it is lower for small or large fractions of asperity contacts. However, it may become higher if the ponding becomes significant. Transport simulations indicate that tracers bypass dead-end pores and travel along flow paths that have less flow resistance. Accordingly, tracer breakthrough curves generally show more spreading than breakthrough curves for water. Further analyses suggest that the log-normal time model generally fails to fit the breakthrough curves for water, but it is a good approximation for breakthrough curves for the tracer.
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.
2013-03-01T23:59:59.000Z
NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.
A Multivariate Baltic Sea Environmental Index Joachim W. Dippner, Georgs Kornilovs,
Dippner, Joachim W.
REPORT A Multivariate Baltic Sea Environmental Index Joachim W. Dippner, Georgs Kornilovs, Karin multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea
Multivariate discrimination and the Higgs + W/Z search
Kevin Black; Jason Gallicchio; John Huth; Michael Kagan; Matthew D. Schwartz; Brock Tweedie
2011-06-21T23:59:59.000Z
A systematic method for optimizing multivariate discriminants is developed and applied to the important example of a light Higgs boson search at the Tevatron and the LHC. The Significance Improvement Characteristic (SIC), defined as the signal efficiency of a cut or multivariate discriminant divided by the square root of the background efficiency, is shown to be an extremely powerful visualization tool. SIC curves demonstrate numerical instabilities in the multivariate discriminants, show convergence as the number of variables is increased, and display the sensitivity to the optimal cut values. For our application, we concentrate on Higgs boson production in association with a W or Z boson with H -> bb and compare to the irreducible standard model background, Z/W + bb. We explore thousands of experimentally motivated, physically motivated, and unmotivated single variable discriminants. Along with the standard kinematic variables, a number of new ones, such as twist, are described which should have applicability to many processes. We find that some single variables, such as the pull angle, are weak discriminants, but when combined with others they provide important marginal improvement. We also find that multiple Higgs boson-candidate mass measures, such as from mild and aggressively trimmed jets, when combined may provide additional discriminating power. Comparing the significance improvement from our variables to those used in recent CDF and DZero searches, we find that a 10-20% improvement in significance against Z/W + bb is possible. Our analysis also suggests that the H + W/Z channel with H -> bb is also viable at the LHC, without requiring a hard cut on the W/Z transverse momentum.
Multivariate Graphs in Software Engineering and A. Telea2
Telea, Alexandru C.
Multivariate Graphs in Software Engineering S. Diehl1 and A. Telea2 1 Department of Computer, the Netherlands Abstract. Multivariate networks, or graphs, are an essential element of various activities. In this chapter, we present the specific context in which multivariate graphs occur in software engineering
MULTIVARIATE REGRESSION S-ESTIMATORS FOR ROBUST ESTIMATION AND INFERENCE
Van Aelst, Stefan
1 MULTIVARIATE REGRESSION S-ESTIMATORS FOR ROBUST ESTIMATION AND INFERENCE Stefan Van Aelst-estimators for multivariate regression. We study the robustness of the estimators in terms of their breakdown point and in and multivariate location and scatter. Furthermore we develop a fast and robust bootstrap method
A Multivariate Approach to Estimate Complexity of FMRI Time Series
A Multivariate Approach to Estimate Complexity of FMRI Time Series Henry SchÂ¨utze1,2 , Thomas (MPSE), a multivariate entropy ap- proach that estimates spatio-temporal complexity of fMRI time series. In a temporally sliding window, MPSE measures the differential entropy of an assumed multivariate Gaussian density
A Hit-or-Miss Transform for Multivariate Images
LefÃ¨vre, SÃ©bastien
A Hit-or-Miss Transform for Multivariate Images E. Aptoula, S. Lef`evre , C. Ronse LSIIT UMR-7005 these definitions to the case of multivariate images, and propose a vectorial HMT, allowing the detection of objects of multivariate data. We addi- tionally present examples of the use of the suggested operator in combination
The Multivariate Merit Factor of a Boolean T. Aaron Gulliver
Parker, Matthew Geoffrey
The Multivariate Merit Factor of a Boolean Function T. Aaron Gulliver Dept. of Electrical, the multivariate merit factor (MMF) of a Boolean function, is presented, and various infinite recursive quadratic sequence constructions are given for which both uni- variate and multivariate merit factors can be computed
The Multivariate Merit Factor of a Boolean T. Aaron Gulliver
Parker, Matthew Geoffrey
The Multivariate Merit Factor of a Boolean Function T. Aaron Gulliver Dept. of Electrical metric, the multivariate merit factor (MMF) of a Boolean function, is presented, and various infinite recursive quadratic sequence constructions are given for which both uniÂ variate and multivariate merit
Testing Multivariate Linear Functions: Overcoming the Generator Bottleneck
Ergun, Funda
Testing Multivariate Linear Functions: Overcoming the Generator Bottleneck Funda ErgÂ¨un \\LambdaÂ testing usually becomes more costly in the case of testing multivariate functions. In this paper we present efficient methods for selfÂtesting multivariate linear functions. We then apply these methods
Partitioning Multivariate Polynomial Equations via Vertex Separators for Algebraic Cryptanal-
International Association for Cryptologic Research (IACR)
Partitioning Multivariate Polynomial Equations via Vertex Separators for Algebraic Cryptanal- ysis. In this paper, we apply similar graph theory techniques to systems of multivariate polynomial equations to a system of multivariate polynomial equations is an NP-complete problem [7, Ch. 3.9]. A variety of solution
Multivariate Gaussian Simulation Outside Arbitrary Nick Ellis and Ranjan Maitra
Maitra, Ranjan
Multivariate Gaussian Simulation Outside Arbitrary Ellipsoids Nick Ellis and Ranjan Maitra Abstract Methods for simulation from multivariate Gaussian distributions restricted to be from outside an arbitrary a multivariate Gaussian distribution and accepts it if it is outside the ellipsoid is often employed: however
Inverses of Multivariate Polynomial Matrices using Discrete Convolution
Young, R. Michael
Inverses of Multivariate Polynomial Matrices using Discrete Convolution R. Lobo Dept. of Elec Raleigh, NC 27695 Abstract-- A new method for inversion of rectangular matrices in a multivariate to multivariate polynomial system of equations is the subject of intensive research and has major applications
Multivariate Selection Response and Estimation of Fitness Surfaces
Walsh, Bruce
Multivariate Selection Response and Estimation of Fitness Surfaces 2nd Annual NSF short course: multivariate selection response (response when selection is acting on a vector of traits) and fitness surface traits. Multivariate Selection Response and Estimation of Fitness Surfaces, pg. 1 #12;* + Select All X Y
XmdvTool: Integrating Multiple Methods for Visualizing Multivariate Data
Ward, Matthew
XmdvTool: Integrating Multiple Methods for Visualizing Multivariate Data Matthew O. Ward ComputerTool which integrates several of the most common methods for projecting multivariÂ ate data onto a two characteristics. This paper describes a system which has been developed for the display of multivariate data
Multivariate Receptor Modeling for Temporally Correlated Data by Using MCMC
Washington at Seattle, University of
Multivariate Receptor Modeling for Temporally Correlated Data by Using MCMC Eun Sug Park Peter Protection Agency which provides the Center's primary funding. #12;Multivariate Receptor Modeling Multivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based
Multivariate High-Frequency-Based Volatility (HEAVY) Models Diaa Noureldin
Wolfe, Patrick J.
Multivariate High-Frequency-Based Volatility (HEAVY) Models Diaa Noureldin Department of Economics.sheppard@economics.ox.ac.uk February 18, 2011 Abstract This paper introduces a new class of multivariate volatility models multivariate GARCH models. We also discuss their covariance targeting speci...cation and provide closed
Pseudo Multivariate Morphological Operators based on -trimmed Lexicographical Extrema
LefÃ¨vre, SÃ©bastien
Pseudo Multivariate Morphological Operators based on -trimmed Lexicographical Extrema Erchan of mathematical morphology to color and more generally to multivariate image data is still an open problem. The definition of multivariate morphological op- erators requires the introduction of a complete lattice struc
N. Panja; A. K. Chattopadhyay
2014-12-05T23:59:59.000Z
We report results of an experimental study, complemented by detailed statistical analysis of the experimental data, on the development of a more effective control method of drug delivery using a pH sensitive acrylic polymer. New copolymers based on acrylic acid and fatty acid are constructed from dodecyl castor oil and a tercopolymer based on methyl methacrylate, acrylic acid and acryl amide were prepared using this new approach. Water swelling characteristics of fatty acid, acrylic acid copolymer and tercopolymer respectively in acid and alkali solutions have been studied by a step-change method. The antibiotic drug cephalosporin and paracetamol have also been incorporated into the polymer blend through dissolution with the release of the antibiotic drug being evaluated in bacterial stain media and buffer solution. Our results show that the rate of release of paracetamol getss affected by the pH factor and also by the nature of polymer blend. Our experimental data have later been statistically analyzed to quantify the precise nature of polymer decay rates on the pH density of the relevant polymer solvents. The time evolution of the polymer decay rates indicate a marked transition from a linear to a strictly non-linear regime depending on the whether the chosen sample is a general copolymer (linear) or a tercopolymer (non-linear). Non-linear data extrapolation techniques have been used to make probabilistic predictions about the variation in weight percentages of retained polymers at all future times, thereby quantifying the degree of efficacy of the new method of drug delivery.
32. Statistics 1 32. STATISTICS
Masci, Frank
32. Statistics 1 32. STATISTICS Revised September 2007 by G. Cowan (RHUL). This chapter gives an overview of statistical methods used in High Energy Physics. In statistics, we are interested in using's validity or to determine the values of its parameters. There are two main approaches to statistical
Statistical Laboratory & Department of Statistics
by the American Statistical Association. Dean Isaacson and Mark Kaiser were instrumental in garnering a NationalStatistical Laboratory & Department of Statistics Annual Report July 1, 2002 to June 30, 2003 IOWA Chair of the Department of Statistics and Director of the Statistical Laboratory in November, 2002. Dean
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM GONGJUN XU Department of Statistics Columbia University Statistical Inference for Diagnostic Classification Models MONDAY, February 18, 2013 at 4:00 PM-driven construction (estimation) of the Q-matrix and related statistical issues of DCMs. I will first give
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM ERNST WIT Statistics and Probability University devices collect a lot of information, typically about few independent statistical subjects or units statistics. In certain special cases the method can be tweaked to obtain L1-penalized GLM solution paths
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM PO-LING LOH Department of Statistics University the seminar in Eckhart 110 ABSTRACT Noisy and missing data are prevalent in many real-world statistical, and provide theoretical guarantees for the statistical consistency of our methods. Although our estimators
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM SAYAN MUKHERJEE Department of Statistical Science vignettes where topological ideas are explored in statistical models of complex traits, machine learning such as sufficient statistics and dictionary learning will be touched on. I will describe an application
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM NOUREDDINE EL KAROUI Department of Statistics will discuss the behavior of widely used statistical methods in the high-dimensional setting where the number surprising statistical phenomena occur: for instance, maximum likelihood methods are shown to be (grossly
Comnes, G.A.; Belden, T.N.; Kahn, E.P.
1995-02-01T23:59:59.000Z
The market for long-term bulk power is becoming increasingly competitive and mature. Given that many privately developed power projects have been or are being developed in the US, it is possible to begin to evaluate the performance of the market by analyzing its revealed prices. Using a consistent method, this paper presents levelized contract prices for a sample of privately developed US generation properties. The sample includes 26 projects with a total capacity of 6,354 MW. Contracts are described in terms of their choice of technology, choice of fuel, treatment of fuel price risk, geographic location, dispatchability, expected dispatch niche, and size. The contract price analysis shows that gas technologies clearly stand out as the most attractive. At an 80% capacity factor, coal projects have an average 20-year levelized price of $0.092/kWh, whereas natural gas combined cycle and/or cogeneration projects have an average price of $0.069/kWh. Within each technology type subsample, however, there is considerable variation. Prices for natural gas combustion turbines and one wind project are also presented. A preliminary statistical analysis is conducted to understand the relationship between price and four categories of explanatory factors including product heterogeneity, geographic heterogeneity, economic and technological change, and other buyer attributes (including avoided costs). Because of residual price variation, we are unable to accept the hypothesis that electricity is a homogeneous product. Instead, the analysis indicates that buyer value still plays an important role in the determination of price for competitively-acquired electricity.
Advanced Multivariate Analysis Tools Applied to Surface Analysis.
Office of Scientific and Technical Information (OSTI)
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5 TablesExports to3,1,50022,3,,0,,6,1,Separation 23TribalInformation Access toTenEnvironment (Conference) | SciTechstabilized by(Conference) |
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM PETER GUTTORP University of Washington and Norwegian Computing Center The Heat Is On! A Statistical Look at the State of the Climate MONDAY, May 6, 2013 at 4
Hastie, Trevor
', Journal of the American Statistical Association 89, 1255-- 1270. Hertz, J., Krogh, A. & Palmer, R. (1991. 1994a). References Becker, R., Chambers, J. & Wilks, A. (1988), The New S Language, WadsworthHastie, T., Tibshirani, R. & Buja, A. (1994b), `Flexible discriminant analysis by optimal scoring
One Statistician's Perspectives on Statistics and "Big Data" Analytics
Vardeman, Stephen B.
One Statistician's Perspectives on Statistics and "Big Data" Analytics Some (Ultimately 2014 Vardeman (Iowa State University) Perspectives on "Big Data" Analytics July 2014 1 / 16 #12;My/modern-multivariate-statistical-learning/ Vardeman (Iowa State University) Perspectives on "Big Data" Analytics July 2014 2 / 16 #12;Some (Indirect
Garcia-Villalba, Manuel; Uhlmann, Markus
2012-01-01T23:59:59.000Z
We have performed a direct numerical simulation of dilute turbulent particulate flow in a vertical plane channel, fully resolving the phase interfaces. The flow conditions are the same as those in the main case of "Uhlmann, M., Phys. Fluids, vol. 20, 2008, 053305", with the exception of the computational domain length which has been doubled in the present study. The statistics of flow and particle motion are not significantly altered by the elongation of the domain. The large-scale columnar-like structures which had previously been identified do persist and they are still only marginally decorrelated in the prolonged domain. Voronoi analysis of the spatial particle distribution shows that the state of the dispersed phase can be characterized as slightly more ordered than random tending towards a homogeneous spatial distribution. It is also found that the p.d.f.'s of Lagrangian particle accelerations for wall-normal and spanwise directions follow a lognormal distribution as observed in previous experiments of ...
Klenzing, J. H.; Earle, G. D.; Heelis, R. A.; Coley, W. R. [William B. Hanson Center for Space Sciences, University of Texas at Dallas, 800 W. Campbell Rd. WT15, Richardson, Texas 75080 (United States)
2009-05-15T23:59:59.000Z
The use of biased grids as energy filters for charged particles is common in satellite-borne instruments such as a planar retarding potential analyzer (RPA). Planar RPAs are currently flown on missions such as the Communications/Navigation Outage Forecast System and the Defense Meteorological Satellites Program to obtain estimates of geophysical parameters including ion velocity and temperature. It has been shown previously that the use of biased grids in such instruments creates a nonuniform potential in the grid plane, which leads to inherent errors in the inferred parameters. A simulation of ion interactions with various configurations of biased grids has been developed using a commercial finite-element analysis software package. Using a statistical approach, the simulation calculates collected flux from Maxwellian ion distributions with three-dimensional drift relative to the instrument. Perturbations in the performance of flight instrumentation relative to expectations from the idealized RPA flux equation are discussed. Both single grid and dual-grid systems are modeled to investigate design considerations. Relative errors in the inferred parameters for each geometry are characterized as functions of ion temperature and drift velocity.
Method for factor analysis of GC/MS data
Van Benthem, Mark H; Kotula, Paul G; Keenan, Michael R
2012-09-11T23:59:59.000Z
The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.
Independent Statistics & Analysis
Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5 TablesExports to3,1,50022,3,,0,,6,1,Separation 23 362 334 318 706Production%3.PDFFeet) YearProduction from1.Foot)Improving WellApril 2015
Independent Statistics & Analysis
Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5 TablesExports to3,1,50022,3,,0,,6,1,Separation 23 362 334 318 706Production% of41.1Diesel prices increaseshort version) TheHow much willIndependent
Independent Statistics & Analysis
Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5 TablesExports to3,1,50022,3,,0,,6,1,Separation 23 362 334 318 706Production% of41.1DieselRegularContact99 Diagram 4.
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM SRIRAM SANKARARAMAN Department of Genetics Harvard Medical School Statistical Models for Analyzing Ancient Human Admixture WEDNESDAY, January 21, 2015, at 4 become available, as well as appropriate statistical models. In the first part of my talk, I will focus
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM ROBERT NOWAK Department of Electrical and Computer-dimensional statistical models to capture the complexity of such problems. Most of the work in this direction has focused of statistical inference. These procedures automatically adapt the measurements in order to focus and optimize
Department of Statistics STATISTICS COLLOQUIUM
Department of Statistics STATISTICS COLLOQUIUM PIOTR ZWIERNIK Department of Mathematics University of Genoa Understanding Statistical Models Through Their Geometry MONDAY, January 26, 2015, at 4:00 PM and Gaussian statistical models have a rich geometric structure and can be often viewed as algebraic sets
Statistical Performance Modeling of SRAMs
Zhao, Chang
2011-02-22T23:59:59.000Z
to their characteristic of low failure rate, while statistical method of yield sensitivity analysis is meaningful for its high efficiency. This thesis proposes a novel statistical model to conduct yield sensitivity prediction on SRAM cells at the simulation level, which...
Hitchcock, Adam P.
Mathematics & Statistics Coop Program Students from the Mathematics & Statistics Coop Program have design and data analysis, medical imaging, mathematical finance and statistical modeling. They have of Mathematics & Statistics Coop Work Terms Duties: Performed data mapping and analysis activities Derived
ORISE: Statistical Analyses of Worker Health
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
appropriate methods of statistical analysis to a variety of problems in occupational health and other areas. Our expertise spans a range of capabilities essential for statistical...
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Series ANDREW C. THOMAS Department of Statistics Harvard University Uncertainties in Network Analysis Due to the Thresholding Problem WEDNESDAY
Multivariate Lipschitz optimization: Survey and computational comparison
Hansen, P.; Gourdin, E.; Jaumard, B.
1994-12-31T23:59:59.000Z
Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.
Department: Statistics Course No: STAT 110Q
Alpay, S. Pamir
Department: Statistics Course No: STAT 110Q Title: Elementary Concepts of Statistics Credits: 4 restrictions above. Standard and nonparametric approaches to statistical analysis; exploratory data analysis-sample procedures, regression and correlation. Learning to do statistical analysis on a personal computer
Department: Statistics Course No: STAT 100Q
Alpay, S. Pamir
Department: Statistics Course No: STAT 100Q Title: Introduction to Statistics Credits: 4 Contact above. A standard approach to statistical analysis primarily for students of business and economics and correlation, exploratory data analysis. Learning to do statistical analysis on a personal computer
Statistical Convergence and Convergence in Statistics
Mark Burgin; Oktay Duman
2006-12-07T23:59:59.000Z
Statistical convergence was introduced in connection with problems of series summation. The main idea of the statistical convergence of a sequence l is that the majority of elements from l converge and we do not care what is going on with other elements. We show (Section 2) that being mathematically formalized the concept of statistical convergence is directly connected to convergence of such statistical characteristics as the mean and standard deviation. At the same time, it known that sequences that come from real life sources, such as measurement and computation, do not allow, in a general case, to test whether they converge or statistically converge in the strict mathematical sense. To overcome limitations induced by vagueness and uncertainty of real life data, neoclassical analysis has been developed. It extends the scope and results of the classical mathematical analysis by applying fuzzy logic to conventional mathematical objects, such as functions, sequences, and series. The goal of this work is the further development of neoclassical analysis. This allows us to reflect and model vagueness and uncertainty of our knowledge, which results from imprecision of measurement and inaccuracy of computation. In the context on the theory of fuzzy limits, we develop the structure of statistical fuzzy convergence and study its properties.
Multivariate orthogonal Laurent polynomials and integrable systems
Gerardo Ariznabarreta; Manuel Mañas
2015-06-30T23:59:59.000Z
An ordering for Laurent polynomials in the algebraic torus $(\\mathbb C^*)^D$, inspired by the Cantero-Moral-Vel\\'azquez approach to orthogonal Laurent polynomials in the unit circle, leads to the construction of a moment matrix for a given Borel measure in the unit torus $\\mathbb T^D$. The Gauss-Borel factorization of this moment matrix allows for the construction of multivariate biorthogonal Laurent polynomials in the unit torus which can be expressed as last quasi-determinants of bordered truncations of the moment matrix. Christoffel type perturbations of the measure given by the multiplication by Laurent polynomials are studied. Sample matrices on poised sets of nodes, which belong to the algebraic hypersurface of the perturbing Laurent polynomial, are used for the finding of a Christoffel formula that expresses the perturbed orthogonal Laurent polynomials in terms of a last quasi-determinant of a bordered sample matrix constructed in terms of the original orthogonal Laurent polynomials. Poised sets exist only for nice Laurent polynomials which are analyzed from the perspective of Newton polytopes and tropical geometry. Discrete and continuous deformations of the measure lead to a Toda type integrable hierarchy, being the corresponding flows described through Lax and Zakharov-Shabat equations; bilinear equations and vertex operators are found. Varying size matrix nonlinear partial difference and differential equations of the 2D Toda lattice type are shown to be solved by matrix coefficients of the multivariate orthogonal polynomials.
American Statistical Association National Science Foundation
Bermúdez, José Luis
American Statistical Association National Science Foundation U.S. Census Bureau ASA American Statistical Association 732 North Washington Street Alexandria, VA 22314-1943 U.S. government and analysis, statistical methodology and computing, information and behavioral science, and geography
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Tiefeng JIANG School of Statistics). A history from 1914 to 2003 of the problem from Mechanics, Statistics and Imagine Analysis will also
Term statistics Zipf's law text statistics
Lu, Jianguo
Term statistics Zipf's law text statistics October 20, 2014 text statistics 1 / 19 #12;Term statistics Zipf's law Overview 1 Term statistics 2 Zipf's law text statistics 2 / 19 #12;Term statistics Zipf's law Outline 1 Term statistics 2 Zipf's law text statistics 3 / 19 #12;Term statistics Zipf's law Model
Lamprecht, William Otto
1967-01-01T23:59:59.000Z
and understanding which made it ail worth while. TABLE OF CONTENTS Page ACKNOWLEDGMENTS LIST OF TABLES LIST OF FIGURES V Vl Chapter I. INTRODUCTIOiN II. EXPERIMENTAL PROCEDURE III. MULTIPLE REGRFSSION ANALYSIS ON DIETARY FEED INTAKE IV. DISCRIMINANT... Including Periods 8. Discriminant Analysis - Preliminary Analysis (17 Discriminants) 28 35 9. Oiscriminant Analysis Lambda \\lalues and Analysis of Variance . 38 10. Discriminant Analysis Lambda Values and Analysis of Variance on Log Transformed Data...
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01T23:59:59.000Z
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
STATISTICS FOR GRADUATE STUDENTS ONLINE RESOURCES
Fletcher, Robin
1 STATISTICS FOR GRADUATE STUDENTS ONLINE RESOURCES Introduction to Data Analysis Exploring Data Pitfalls of Data Analysis (or How to Avoid Lies and Damned Lies) Statistics notes and related articles published in British Medical Journal Statistical literacy HyperStat Online Statistics Textbook Electronic
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Samuel Kou Department of Statistics statistical modeling and inference effort. This paper provides the first likelihood-based analysis statistical techniques to the analysis of data produced by modern technologies. This work is joint with Sunney
Two tests for multivariate normality based on the characteristic function
Arcones, Miguel A.
Two tests for multivariate normality based on the characteristic function Miguel A. Arcones-mail:arcones@math.binghamton.edu April 10, 2007 Abstract We present two tests for multivariate normality. The presented tests are based on the L´evy characterization of the normal distribution and on the BHEP tests. The tests are affine
Multivariate Multi-Model Approach for Globally Multimodal Problems
Chu, Hao-hua
Multivariate Multi-Model Approach for Globally Multimodal Problems Chung-Yao Chuang Institute this source of difficulty, we designed an EDA that builds and samples multiple probabilistic models at each adopt multivariate probabilis- tic models. Furthermore, we have also devised a mechanism
A Multivariate Probabilistic Method for Comparing Two Clinical Datasets
Hauskrecht, Milos
A Multivariate Probabilistic Method for Comparing Two Clinical Datasets Yuriy Sverchkov yus24@pitt a concise and math- ematically grounded description of multivariate differences between a pair of clinical (ICUs), or within the same ICU during different periods, may show systematically different outcomes
Chapter 40: Multivariate autoregressive models W. Penny and L. Harrison
Penny, Will
- anisms that may generate data. State-Space Models account for correlations within the data by invoking an approach based on Multivariate Autoregressive (MAR) models. These are linear multivariate time series correlations. We use MAR models to make inferences about functional integration from fMRI data. The chapter
Multivariable Discrete Time Repetitive Control System Hammoud Saari1
Boyer, Edmond
Multivariable Discrete Time Repetitive Control System Hammoud Saari1 and Bernard Caron2 1 SETRAM, France hammoud.saari@yahoo.fr, bernard.caron@univ-savoie.fr Keywords: Repetitive Control, Multivariable), Ahn et al. (2007) and Saari et al. (2010)). Most of their works were focused on the problem
Multivariate wavelet kernel regression method Samir Touzani, Daniel Busbya
Paris-Sud XI, Université de
Multivariate wavelet kernel regression method Samir Touzani, Daniel Busbya a IFP Energies nouvelles multivariate nonparametric regression method, in the framework of wavelet decomposition. We call this method the wavelet kernel ANOVA (WK-ANOVA), which is a wavelet based reproducing kernel Hilbert space (RKHS) method
Moment Based Dimension Reduction for Multivariate Response Regression
Bura, Efstathia
Moment Based Dimension Reduction for Multivariate Response Regression Xiangrong Yin Efstathia Bura January 20, 2005 Abstract Dimension reduction aims to reduce the complexity of a regression without re- quiring a pre-specified model. In the case of multivariate response regressions, covariance
Residential solar home resale analysis
Noll, S.A.
1980-01-01T23:59:59.000Z
One of the determinants of the market acceptance of solar technologies in the residential housing sector is the value placed upon the solar property at the time of resale. The resale factor is shown to be an important economic parameter when net benefits of the solar design are considered over a typical ownership cycle rather than the life cycle of the system. Although a study of solar resale in Davis, Ca, indicates that those particular homes have been appreciating in value faster than nonsolar market comparables, no study has been made that would confirm this conclusion for markets in other geograhical locations with supporting tests of statistical significance. The data to undertake such an analysis is available through numerous local sources; however, case by case data collection is prohibitively expensive. A recommended alternative approach is to make use of real estate market data firms who compile large data bases and provide multi-variate statistical analysis packages.
Multivariate Central Limit Theorem in Quantum Dynamics
Simon Buchholz; Chiara Saffirio; Benjamin Schlein
2013-09-06T23:59:59.000Z
We consider the time evolution of $N$ bosons in the mean field regime for factorized initial data. In the limit of large $N$, the many body evolution can be approximated by the non-linear Hartree equation. In this paper we are interested in the fluctuations around the Hartree dynamics. We choose $k$ self-adjoint one-particle operators $O_1, \\dots, O_k$ on $L^2 (\\R^3)$, and we average their action over the $N$-particles. We show that, for every fixed $t \\in \\R$, expectations of products of functions of the averaged observables approach, as $N \\to \\infty$, expectations with respect to a complex Gaussian measure, whose covariance matrix can be expressed in terms of a Bogoliubov transformation describing the dynamics of quantum fluctuations around the mean field Hartree evolution. If the operators $O_1, \\dots, O_k$ commute, the Gaussian measure is real and positive, and we recover a "classical" multivariate central limit theorem. All our results give explicit bounds on the rate of the convergence (we obtain therefore Berry-Ess{\\'e}en type central limit theorems).
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01T23:59:59.000Z
in high-dimensional multivariate regression. ” The Annals ofSupport Union Recovery in Multivariate Multi-Response LinearSupport Union Recovery in Multivariate Multi-Response Linear
Design and Usability of an Enhanced Geographic Information System for Exploration of Multivariate
Klippel, Alexander
Design and Usability of an Enhanced Geographic Information System for Exploration of Multivariate have recently developed methods for exploring and visualizing large, multivariate datasets with spatial and spatiotemporal datasets that are multivariate in character. This article will present
Grunwald, Sabine
Comparison of multivariate methods for inferential modeling of soil carbon using visible Diffuse reflectance spectroscopy Visible/near-infrared spectroscopy Multivariate calibration Pre multivariate techniques (stepwise multiple linear regression, principal components regression, partial least
Levitin, Daniel
Multivariate Activation and Connectivity Patterns Discriminate Speech Intelligibility in Wernicke. Here we use functional magnetic resonance imaging with a novel whole-brain multivariate pattern relies on differential multivariate response and connectivity patterns in Wernicke's, Broca
Greenberg, Albert
Iterative Multivariate Regression Model for Correlated Responses Prediction S. Tom Au, Guangqin Ma- tive procedure to model multiple responses prediction into correlated multivariate predicting scheme, which is always favorable for responses separations in our multivariate prediction. We also point out
Department: Statistics Course No: STAT 242Q
Alpay, S. Pamir
Department: Statistics Course No: STAT 242Q Title: Analysis of Experiments Credits: 3 Contact Information : A. The objective of this course is to introduce students to statistical data analysis using will obtain hands-on experience with analyzing data usign statistical models, such as regression and analysis
Chang, Wen-Kuei; Hong, Tianzhen
2013-01-01T23:59:59.000Z
Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.
Michele Arzano; Dario Benedetti
2008-09-04T23:59:59.000Z
Non-commutative quantum field theories and their global quantum group symmetries provide an intriguing attempt to go beyond the realm of standard local quantum field theory. A common feature of these models is that the quantum group symmetry of their Hilbert spaces induces additional structure in the multiparticle states which reflects a non-trivial momentum-dependent statistics. We investigate the properties of this "rainbow statistics" in the particular context of $\\kappa$-quantum fields and discuss the analogies/differences with models with twisted statistics.
MODELING OF MULTIVARIATE INTERACTIONS THROUGH THEIR MANIFESTATIONS AND LOW DIMENSIONAL MODEL
Cirpka, Olaf Arie
MODELING OF MULTIVARIATE INTERACTIONS THROUGH THEIR MANIFESTATIONS AND LOW DIMENSIONAL MODEL, of which mutual information 1 #12;MODELING OF MULTIVARIATE INTERACTIONS 2 is one particular case
Application of a single multivariable controller to an FCCU
Cutler, C.R.; Johnston, C.R.; Raven, D.B. (Dynamic Matrix Control Corp., Houston, TX (United States)); Eakens, R.W.; Koepke, J. (Star Enterprise, Port Arthur, TX (United States)); Alrushaid, N. (Saudi ARAMCO, Ras Tanura (Saudi Arabia))
1993-01-01T23:59:59.000Z
A number of significant benefits are realized from the design and operation of a single multivariable controller for a Fluid Catalytic Cracking Unit. A single controller has been built for the Star Enterprise FCCU No. 3 in Port Arthur, Texas. The controller includes the Feed Preheat System, the Reactor, the Regenerator, the Main Fractionator, and the Wet Gas Compression. The controller contains 17 manipulated variables, 41 controlled variables, and 1 disturbance variable. The elapse time between project initiation and final commissioning was four months including a one month delay for unit maintenance. After commissioning, it was determined in the post audit that the simple payout for the project was less than one month. The controller has maintained a high stream factor since its commissioning 8 months ago. The single large scale controller improves the reliability of the control system, permits the handling of all the interactions between independent variables, removes stability analysis from the controller design, increases the ability of the controller to address the economics of the operation, and increases the maintainability of the system relative to traditional heuristic combinations of PID controllers.
Multivariate Techniques for Identifying Diffractive Interactions at the LHC
Mikael Kuusela; Jerry W. Lamsa; Eric Malmi; Petteri Mehtala; Risto Orava
2009-09-16T23:59:59.000Z
Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.
Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions
Carreira-Perpinan, Miguel A; Renals, Steve
The class of finite mixtures of multivariate Bernoulli distributions is known to be nonidentifiable; that is, different values of the mixture parameters can correspond to exactly the same probability distribution. In ...
Synthesis of reduced order prefilters for multivariable tracking
Bement, Matthew Thomas
1997-01-01T23:59:59.000Z
A primary disadvantage of using an internal model to achieve multivariable tracking is the high order of the internal model. In situations where it is known that each output is to track only its associated reference input, the internal model...
Topics on Regularization of Parameters in Multivariate Linear Regression
Chen, Lianfu
2012-02-14T23:59:59.000Z
My dissertation mainly focuses on the regularization of parameters in the multivariate linear regression under different assumptions on the distribution of the errors. It consists of two topics where we develop iterative procedures to construct...
Multi-variable optimization of pressurized oxy-coal combustion
Zebian, Hussam
2011-01-01T23:59:59.000Z
Simultaneous multi-variable gradient-based optimization with multi-start is performed on a 300 MWe wet-recycling pressurized oxy-coal combustion process with carbon capture and sequestration. The model accounts for realistic ...
Mathematics and statistics research department. Progress report, period ending June 30, 1981
Lever, W.E.; Kane, V.E.; Scott, D.S.; Shepherd, D.E.
1981-09-01T23:59:59.000Z
This report is the twenty-fourth in the series of progress reports of the Mathematics and Statistics Research Department of the Computer Sciences Division, Union Carbide Corporation - Nuclear Division (UCC-ND). Part A records research progress in biometrics research, materials science applications, model evaluation, moving boundary problems, multivariate analysis, numerical linear algebra, risk analysis, and complementary areas. Collaboration and consulting with others throughout the UCC-ND complex are recorded in Part B. Included are sections on biology and health sciences, chemistry, energy, engineering, environmental sciences, health and safety research, materials sciences, safeguards, surveys, and uranium resource evaluation. Part C summarizes the various educational activities in which the staff was engaged. Part D lists the presentations of research results, and Part E records the staff's other professional activities during the report period.
Statistics and Philosophy Mathematical models and reality
Hennig, Christian
Statistics and Philosophy Mathematical models and reality Frequentist probabilities The Bayes-frequentist controversy Cluster analysis and truth Model Assumptions and Truth in Statistics Christian Hennig 4 February 2015 Christian Hennig Model Assumptions and Truth in Statistics #12;Statistics and Philosophy
Department: Statistics Course No: STAT 243Q
Alpay, S. Pamir
Department: Statistics Course No: STAT 243Q Title: Design of Experiments Credits: 3 Contact : Dipak statistical software to analize data sets based on the statistical methods developed in this course. B. Two and problem assignments from the textbook. C. Analysis of variance statistical models are the major theme
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Statistics Colloquium Series PATRICK J. WOLFE Department of Statistics Harvard University Modeling Network Data MONDAY, October 10, 2011, at 4:00 PM 133 Networks are fast becoming a primary object of interest in statistical data analysis, with important
Callen, Elisabeth F.
2012-12-31T23:59:59.000Z
Mesoscale Convective Systems (MCSs) are the focus of this analysis since it is the convective weather category which is smallest in number but produces the highest amount of precipitation. Being able to forecast these MCSs ...
Wavelet analysis of the multivariate fractional Brownian motion
Paris-Sud XI, Université de
,2 , Pierre-Olivier Amblard2,3 and Sophie Achard2 1 Laboratory Jean Kuntzmann, Grenoble University, France, 2, 2009; Achard et al., 2008, 2006). In all these disciplines, many modern sensing approaches allow
Assessment of Critical Events Corridors through Multivariate Cascading Outages Analysis
Makarov, Yuri V.; Samaan, Nader A.; Diao, Ruisheng; Kumbale, Murali; Chen, Yousu; Singh, Ruchi; Green, Irina; Morgan, Mark P.
2011-10-17T23:59:59.000Z
Massive blackouts of electrical power systems in North America over the past decade has focused increasing attention upon ways to identify and simulate network events that may potentially lead to widespread network collapse. This paper summarizes a method to simulate power-system vulnerability to cascading failures to a supplied set of initiating events synonymously termed as Extreme Events. The implemented simulation method is currently confined to simulating steady state power-system response to a set of extreme events. The outlined method of simulation is meant to augment and provide a new insight into bulk power transmission network planning that at present remains mainly confined to maintaining power system security for single and double component outages under a number of projected future network operating conditions. Although one of the aims of this paper is to demonstrate the feasibility of simulating network vulnerability to cascading outages, a more important goal has been to determine vulnerable parts of the network that may potentially be strengthened in practice so as to mitigate system susceptibility to cascading failures. This paper proposes to demonstrate a systematic approach to analyze extreme events and identify vulnerable system elements that may be contributing to cascading outages. The hypothesis of critical events corridors is proposed to represent repeating sequential outages that can occur in the system for multiple initiating events. The new concept helps to identify system reinforcements that planners could engineer in order to 'break' the critical events sequences and therefore lessen the likelihood of cascading outages. This hypothesis has been successfully validated with a California power system model.
A Multivariate Analysis of Freeway Speed and Headway Data
Zou, Yajie
2013-11-11T23:59:59.000Z
The knowledge of speed and headway distributions is essential in microscopic traffic flow studies because speed and headway are both fundamental microscopic characteristics of traffic flow. For microscopic ...
Arrowood, L.F.; Tonn, B.E.
1992-02-01T23:59:59.000Z
This report presents recommendations relative to the use of expert systems and machine learning techniques by the Bureau of Labor Statistics (BLS) to substantially automate product substitution decisions associated with the Consumer Price Index (CPI). Thirteen commercially available, PC-based expert system shells have received in-depth evaluations. Various machine learning techniques were also reviewed. Two recommendations are given: (1) BLS should use the expert system shell LEVEL5 OBJECT and establish a software development methodology for expert systems; and (2) BLS should undertake a small study to evaluate the potential of machine learning techniques to create and maintain the approximately 350 ELI-specific knowledge bases to be used in CPI product substitution review.
Bill Jackson; Aldo Procacci; Alan D. Sokal
2014-12-02T23:59:59.000Z
We find zero-free regions in the complex plane at large |q| for the multivariate Tutte polynomial (also known in statistical mechanics as the Potts-model partition function) Z_G(q,w) of a graph G with general complex edge weights w = {w_e}. This generalizes a result of Sokal (cond-mat/9904146) that applies only within the complex antiferromagnetic regime |1+w_e| \\le 1. Our proof uses the polymer-gas representation of the multivariate Tutte polynomial together with the Penrose identity.
Jan de Leeuw
2011-01-01T23:59:59.000Z
with Real Data ? Annals Statistics, of 5:1055-1098, 1977.The Foundations of Statistics - Are There Any ? Synthese, [Statistics and the Sciences Jan de Leeuw UCLA Statistics
Precision in multivariate optical computing Frederick G. Haibach and Michael L. Myrick
Myrick, Michael Lenn
Precision in multivariate optical computing Frederick G. Haibach and Michael L. Myrick Multivariate, the instrument implements a multivariate regression vector whose dot product with the spectrum yields a single-signal-limited performance of MOC instrumentation. These two general expressions are applied to the traditional multivariate
A Multivariate based Threshold Ring Signature Scheme Albrecht Petzoldt1,2
International Association for Cryptologic Research (IACR)
A Multivariate based Threshold Ring Signature Scheme Albrecht Petzoldt1,2 , Stanislav Bulygin1.buchmann,Stanislav.Bulygin}@cased.de Abstract. In [16], Sakumoto et al. presented a new multivariate identification scheme, whose security is the first multivariate scheme of this type and generally the first multivariate signature scheme
Proposal for Piece In Hand Matrix Ver.2: General Concept for Enhancing Security of Multivariate
International Association for Cryptologic Research (IACR)
Proposal for Piece In Hand Matrix Ver.2: General Concept for Enhancing Security of Multivariate of multivariate public key cryptosystems to enhance their security. In this paper, we make improvements in the PH cryptosystem, multivariate polynomial, multivariate public key cryptosystem, piece in hand concept, soldiers
On the Number of Tight Wavelet Frame Generators associated with Multivariate Box Splines
Lai, Ming-Jun
Kronecker poduct method in [2]. Keywords Sum of square magnitudes, Multivariate box splines, Tight wavelet
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Series MATHIAS DRTON Department of Statistics The University of Chicago "Algebraic Factor Analysis: Tetrads, Pentads and Beyond" MONDAY, January in Eckhart 110. ABSTRACT Abstract: Factor analysis refers to a statistical model in which observed variables
Department: Statistics Course No: STAT 272Q
Alpay, S. Pamir
Department: Statistics Course No: STAT 272Q Title: Introduction to Biostatistics Credits: 3 Contact-analysis. Course Information : A. The goal is to introduce the students to the modern statistical methods in modeling analysis of data in biological and medical sciences. Teach the students the use of statistical
Statistical Mechanics, CHEM 6481, Fall MWF 10:05-10:55am, MoSE 1224
Sherrill, David
Statistical Mechanics, CHEM 6481, Fall MWF 10:05-10:55am, MoSE 1224 Course website: T-Square Prerequisites: Advanced quantum mechanics (can be taken concurrently), advanced thermodynamics, multivariate" are considered background material. Required Text: "Introduction to Modern Statistical Mechanics," David Chandler
Hunter, David
, following the publication of Venables & Ripley (1994). [See p. 1. Where that takes a significantly betterÂPlus include dynamic graphics (x6.3, brush and spin) and the classical statistics functions (x9 environment. It will help to have the library ripley available --- it should be in the same source
A multivariate phase distribution and its estimation
Charles F. Cadieu; Kilian Koepsell
2009-06-21T23:59:59.000Z
Circular variables such as phase or orientation have received considerable attention throughout the scientific and engineering communities and have recently been quite prominent in the field of neuroscience. While many analytic techniques have used phase as an effective representation, there has been little work on techniques that capture the joint statistics of multiple phase variables. In this paper we introduce a distribution that captures empirically observed pair-wise phase relationships. Importantly, we have developed a computationally efficient and accurate technique for estimating the parameters of this distribution from data. We show that the algorithm performs well in high-dimensions (d=100), and in cases with limited data (as few as 100 samples per dimension). We also demonstrate how this technique can be applied to electrocorticography (ECoG) recordings to investigate the coupling of brain areas during different behavioral states. This distribution and estimation technique can be broadly applied to any setting that produces multiple circular variables.
Four Faculty Positions Applied Statistics & Computational Statistics
Shepp, Larry
Four Faculty Positions Applied Statistics & Computational Statistics The Department of Statistics at the Assistant Professor rank. Two positions are open in the area of Applied Statistics, with a focus on the development of statistical methodology and statistical consulting. The other two positions are open
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Series STEFFEN LAURITZEN Department of Statistics University of Oxford Bayesian Networks for the Analysis of DNA Mixtures MONDAY, May 21, 2009, at 4
Intrinsic alignments of galaxies in the MassiveBlack-II simulation: Analysis of two-point statistics
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Tenneti, Ananth [Carnegie Mellon Univ., Pittsburgh, PA (United States); Khandai, Nishikanta [Brookhaven National Lab. (BNL), Upton, NY (United States); National Inst. of Science Education and Research, Bhubaneswar, Odish (India); Singh, Sukhdeep [Carnegie Mellon Univ., Pittsburgh, PA (United States); Mandelbaum, Rachel [Carnegie Mellon Univ., Pittsburgh, PA (United States); Di Matteo, Tiziana [Carnegie Mellon Univ., Pittsburgh, PA (United States); Feng, Yu [Carnegie Mellon Univ., Pittsburgh, PA (United States)
2015-03-04T23:59:59.000Z
The intrinsic alignment of galaxies with the large-scale density field in an important astrophysical contaminant in upcoming weak lensing surveys. We present detailed measurements of the galaxy intrinsic alignments and associated ellipticity-direction (ED) and projected shape (wg?) correlation functions for galaxies in the cosmological hydrodynamic MassiveBlack-II (MB-II) simulation. We carefully assess the effects on galaxy shapes, misalignment of the stellar component with the dark matter shape and two-point statistics of iterative weighted (by mass and luminosity) definitions of the (reduced and unreduced) inertia tensor. We find that iterative procedures must be adopted for a reliable measurement of the reduced tensor but that luminosity versus mass weighting has only negligible effects. Both ED and wg? correlations increase in amplitude with subhalo mass (in the range of 10¹? – 6.0 X 10¹?h?¹ M), with a weak redshift dependence (from z – 1 to z – 0.06) at fixed mass. At z ~ 0.3, we predict a wg? that is in reasonable agreement with SDSS LRG measurements and that decreases in amplitude by a factor of ~ 5–18 for galaxies in the LSST survey. We also compared the intrinsic alignment of centrals and satellites, with clear detection of satellite radial alignments within the host halos. Finally, we show that wg? (using subhalos as tracers of density and w? (using dark matter density) predictions from the simulations agree with that of non-linear alignment models (NLA) at scales where the 2-halo term dominates in the correlations (and tabulate associated NLA fitting parameters). The 1-halo term induces a scale dependent bias at small scales which is not modeled in the NLA model.
Intrinsic alignments of galaxies in the MassiveBlack-II simulation: Analysis of two-point statistics
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Tenneti, Ananth; Khandai, Nishikanta; Singh, Sukhdeep; Mandelbaum, Rachel; Di Matteo, Tiziana; Feng, Yu
2015-03-04T23:59:59.000Z
The intrinsic alignment of galaxies with the large-scale density field in an important astrophysical contaminant in upcoming weak lensing surveys. We present detailed measurements of the galaxy intrinsic alignments and associated ellipticity-direction (ED) and projected shape (wg?) correlation functions for galaxies in the cosmological hydrodynamic MassiveBlack-II (MB-II) simulation. We carefully assess the effects on galaxy shapes, misalignment of the stellar component with the dark matter shape and two-point statistics of iterative weighted (by mass and luminosity) definitions of the (reduced and unreduced) inertia tensor. We find that iterative procedures must be adopted for a reliable measurement of the reduced tensormore »but that luminosity versus mass weighting has only negligible effects. Both ED and wg? correlations increase in amplitude with subhalo mass (in the range of 10¹? – 6.0 X 10¹?h?¹ M), with a weak redshift dependence (from z – 1 to z – 0.06) at fixed mass. At z ~ 0.3, we predict a wg? that is in reasonable agreement with SDSS LRG measurements and that decreases in amplitude by a factor of ~ 5–18 for galaxies in the LSST survey. We also compared the intrinsic alignment of centrals and satellites, with clear detection of satellite radial alignments within the host halos. Finally, we show that wg? (using subhalos as tracers of density and w? (using dark matter density) predictions from the simulations agree with that of non-linear alignment models (NLA) at scales where the 2-halo term dominates in the correlations (and tabulate associated NLA fitting parameters). The 1-halo term induces a scale dependent bias at small scales which is not modeled in the NLA model.« less
Cook, Di
Effects Research Laboratory, Western Ecology Division, Corvallis, OR 97333 5 OAO, c/o USEPA NHEERL Western Ecology Division, Corvallis, OR 97333 Abstract This paper discusses the extent of virtual reality technology and raises an example of using a highly immersive environment for exploring and mining
Multivariate Statistics of the Jacobian Matrices in Tensor Based Morphometry and Their
Thompson, Paul
to HIV/AIDS Natasha Lepore1 , Caroline A. Brun1 , Ming-Chang Chiang1 , Yi-Yu Chou1 , Rebecca A. Dutton1 , and Paul M. Thompson1 1 Laboratory of Neuro Imaging, Department of Neurology, David Geffen School
2001, Applied Statistics, 50, 143-154. Nonlinear autoregressive time series with multivariate
Stone, J. V.
and Collares Pereira, 1992; Beyer et al., 1995, models for mean instantaneous sky luminance distribution have a value greater than unity when the sky is clear and a value less than unity when the sky is cloudy. Its
Parallel auto-correlative statistics with VTK.
Pebay, Philippe Pierre [Kitware, France; Bennett, Janine Camille
2013-08-01T23:59:59.000Z
This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.
David, Mathieu; Garde, Francois; Boyer, Harry
2014-01-01T23:59:59.000Z
In building studies dealing about energy efficiency and comfort, simulation software need relevant weather files with optimal time steps. Few tools generate extreme and mean values of simultaneous hourly data including correlation between the climatic parameters. This paper presents the C++ Runeole software based on typical weather sequences analysis. It runs an analysis process of a stochastic continuous multivariable phenomenon with frequencies properties applied to a climatic database. The database analysis associates basic statistics, PCA (Principal Component Analysis) and automatic classifications. Different ways of applying these methods will be presented. All the results are stored in the Runeole internal database that allows an easy selection of weather sequences. The extreme sequences are used for system and building sizing and the mean sequences are used for the determination of the annual cooling loads as proposed by Audrier-Cros (Audrier-Cros, 1984). This weather analysis was tested with the datab...
Fitelson, Branden
Branden Fitelson Remarks on the Philosophy of Statistics 0 SOME REMARKS ON THE PHILOSOPHY OF STATISTICS BRANDEN FITELSON Department of Philosophy San Jos´e State University branden@fitelson.org http of Statistics 1 Overview of Presentation · What are the ends of statistical experiment, analysis
J. Mark Heinzle; Claes Uggla
2012-12-21T23:59:59.000Z
In this paper we explore stochastical and statistical properties of so-called recurring spike induced Kasner sequences. Such sequences arise in recurring spike formation, which is needed together with the more familiar BKL scenario to yield a complete description of generic spacelike singularities. In particular we derive a probability distribution for recurring spike induced Kasner sequences, complementing similar available BKL results, which makes comparisons possible. As examples of applications, we derive results for so-called large and small curvature phases and the Hubble-normalized Weyl scalar.
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:5(Million Cubic Feet) Oregon (Including Vehicle Fuel) (MillionStructural Basis of Wnt Recognition by Frizzled SSRLDr. Donald L. Cook Foron516 Statistical
Multivariate Non-Normality in the WMAP 1st Year Data
Patrick Dineen; Peter Coles
2005-11-29T23:59:59.000Z
The extraction of cosmological parameters from microwave background observations relies on specific assumptions about the statistical properties of the data, in particular that the p-point distributions of temperature fluctuations are jointly-normal. Using a battery of statistical tests, we assess the multivariate Gaussian nature of the Wilkinson Microwave Anisotropy Probe (WMAP) 1st year data. The statistics we use fall into three classes which test different aspects of joint-normality: the first set assess the normality of marginal (one-point) distributions using familiar univariate methods; the second involves statistics that directly assess joint-normality; and the third explores the evidence of non-linearity in the relationship between variates. We applied these tests to frequency maps, `foreground-cleaned' assembly maps and all-sky CMB-only maps. The assembly maps are of particular interest as when combined with the kp2 mask, we recreate the region used in the computation of the angular power spectrum. Significant departures from normality were found in all the maps. In particular, the kurtosis coefficient, D'Agostino's statistic and bivariate kurtosis calculated from temperature pairs extracted from all the assembly maps were found to be non-normal at 99% confidence level. We found that the results were unaffected by the size of the Galactic cut and were evident on either hemisphere of the CMB sky. The latter suggests that the non-Gaussianity is not simply related to previous claims of north-south asymmetry or localized abnormalities detected through wavelet techniques.
Pareto efficiency for the concave order and multivariate comonotonicity
Paris-Sud XI, Université de
Pareto efficiency for the concave order and multivariate comonotonicity G. Carlier , R.-A. Dana , A. Galichon April 7, 2010 Abstract This paper studues efficient risk-sharing rules for the concave to Landsberger and Meilijson [28], that efficiency is characterized by a comonotonicity condition. The goal
Multivariate classification of infrared spectra of cell and tissue samples
Haaland, David M. (Albuquerque, NM); Jones, Howland D. T. (Albuquerque, NM); Thomas, Edward V. (Albuquerque, NM)
1997-01-01T23:59:59.000Z
Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.
A new sliced inverse regression method for multivariate response regression
reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. We discuss the choice of the dimension of the EDR space. The numerical a way to cluster components of y related to the same EDR space. One can thus apply properly multivariate
Fast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri
Oza, Nikunj C.
search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence approach" may include searching on parameters such as speed, descent rate, vertical flight pathFast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri MCT Inc., NASA ARC
Demkin, V. P.; Mel'nichuk, S. V. [National Research Tomsk State University, 36, Lenin Ave., 634050 Tomsk (Russian Federation)
2014-09-15T23:59:59.000Z
In the present work, results of investigations into the dynamics of secondary electrons with helium atoms in the presence of the reverse electric field arising in the flare of a high-voltage pulsed beam-type discharge and leading to degradation of the primary electron beam are presented. The electric field in the discharge of this type at moderate pressures can reach several hundred V/cm and leads to considerable changes in the kinetics of secondary electrons created in the process of propagation of the electron beam generated in the accelerating gap with a grid anode. Moving in the accelerating electric field toward the anode, secondary electrons create the so-called compensating current to the anode. The character of electron motion and the compensating current itself are determined by the ratio of the field strength to the concentration of atoms (E/n). The energy and angular spectra of secondary electrons are calculated by the Monte Carlo method for different ratios E/n of the electric field strength to the helium atom concentration. The motion of secondary electrons with threshold energy is studied for inelastic collisions of helium atoms and differential analysis is carried out of the collisional processes causing energy losses of electrons in helium for different E/n values. The mechanism of creation and accumulation of slow electrons as a result of inelastic collisions of secondary electrons with helium atoms and selective population of metastable states of helium atoms is considered. It is demonstrated that in a wide range of E/n values the motion of secondary electrons in the beam-type discharge flare has the character of drift. At E/n values characteristic for the discharge of the given type, the drift velocity of these electrons is calculated and compared with the available experimental data.
BS in STATISTICS: Biostatistics Emphasis (695233) MAP Sheet Department of Statistics
Dahl, David B.
Analysis of Variance Stat 240 Discrete Probability Stat 290 Communication of Statistical Results Stat 330 of Survey Sampling Stat 274 Theory of Interest Stat 424 Statistical Computing Stat 431 Experimental Design Theory of Analysis 2 Stat 151 Introduction to Bayesian Statistics Stat 234 Methods of Survey Sampling
New likelihoods for shape analysis
Fichet, Sylvain
2014-01-01T23:59:59.000Z
We introduce a new kind of likelihood function based on the sequence of moments of the data distribution. Both binned and unbinned data samples are discussed, and the multivariate case is also derived. Building on this approach we lay out the formalism of shape analysis for signal searches. In addition to moment-based likelihoods, standard likelihoods and approximate statistical tests are provided. Enough material is included to make the paper self-contained from the perspective of shape analysis. We argue that the moment-based likelihoods can advantageously replace unbinned standard likelihoods for the search of non-local signals, by avoiding the step of fitting Monte-Carlo generated distributions. This benefit increases with the number of variables simultaneously analyzed. The moment-based signal search is exemplified and tested in various 1D toy models mimicking typical high-energy signal--background configurations. Moment-based techniques should be particularly appropriate for the searches for effective o...
Statistical Software - Overview
Jan De Leeuw
2011-01-01T23:59:59.000Z
Review of Statistical Software. International As- sociationStatistical Methods Need Software: A View of Statisti- calJournal of Statistical Software, 13, 2004. URL http://www.
Statistical Software - Overview
Leeuw, Jan De
2010-01-01T23:59:59.000Z
Review of Statistical Software. International As- sociationStatistical Methods Need Software: A View of Statisti- calJournal of Statistical Software, 13, 2004. URL http://www.
Generalizations of quantum statistics
O. W. Greenberg
2008-05-02T23:59:59.000Z
We review generalizations of quantum statistics, including parabose, parafermi, and quon statistics, but not including anyon statistics, which is special to two dimensions.
Computing and Introductory Statistics
Kaplan, Daniel
2007-01-01T23:59:59.000Z
W. , (2007) “The Introductory Statistics Course: A PtolemaicTechnology Innovations in Statistics Education,1, Article 1.and Introductory Statistics Daniel T. Kaplan Macalester
18.441 Statistical Inference, Spring 2002
Hardy, Michael
Reviews probability and introduces statistical inference. Point and interval estimation. The maximum likelihood method. Hypothesis testing. Likelihood-ratio tests and Bayesian methods. Nonparametric methods. Analysis of ...
CAPES 2013 PROBABILITY and STATISTICS Ttulo ISSN
Moreira, Carlos Gustavo
-0918 Communications Series A1 Mathematics & Statistics 1303-5911 Computational and mathematical organization theory-5483 Communications in Mathematical Physics 0010-3616 Communications in statistics. Simulation and computation 0361 communications in probability 1083-589X Electronic journal of applied statistical analysis 2070-5948 Electronic
Spatial Autocorrelation and Statistical Tests: Some Solutions
Fortin, Marie Josee
.fortin@utoronto.ca). 188 © 2009 American Statistical Association and the International Biometric Society JournalSpatial Autocorrelation and Statistical Tests: Some Solutions Mark R. T. DALE and Marie problem in analysis, affecting the significance rates of statistical tests, making them too liberal when
The University of Chicago Department of Statistics
The University of Chicago Department of Statistics Seminar Series GILLES BLANCHARD Associate researcher, Fraunhofer FIRST (IDA) "Statistical Performance of Support Vector Machines" MONDAY, January 29 in Eckhart 110. ABSTRACT I will present a contribution to the statistical analysis of Support Vector Machines
Mathematics/Statistics College of Science MATH-BS Code-MASI ...
Susan Kaye Aufderheide
2013-05-15T23:59:59.000Z
STAT 35000 Introduction To Statistics (satisfies Statistics Requirement). (3). MA 34100 Foundations Of Analysis or MA 44000 Real Analysis Honors. (3).
Johnstone, Daniel; Milward, Elizabeth A.; Berretta, Regina; Moscato, Pablo
2012-01-01T23:59:59.000Z
Multivariate Protein Signatures of Pre-Clinical Alzheimer’s= 2.3610 213 ). We applied a multivariate approach based onInitiative (2012) Multivariate Protein Signatures of Pre-
Paul T. Baker; Sarah Caudill; Kari A. Hodge; Dipongkar Talukder; Collin Capano; Neil J. Cornish
2014-12-19T23:59:59.000Z
Searches for gravitational waves produced by coalescing black hole binaries with total masses $\\gtrsim25\\,$M$_\\odot$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass $\\left( \\gtrsim25\\,\\mathrm{M}_\\odot \\right)$ parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between $25\\,$M$_\\odot$ and $100\\,$M$_\\odot$, we find sensitive volume improvements as high as $70_{\\pm 13}-109_{\\pm 11}$\\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between $10\\,$M$_\\odot$ and $600\\,$M$_\\odot$, we find sensitive volume improvements as high as $61_{\\pm 4}-241_{\\pm 12}$\\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.
Dangl, Jeff
A Poisson-multivariate normal hierarchical model for measuring microbial conditional independence plate, or sequencing depth. In this work, we develop a Poisson-multivariate normal hierarchical model at the multivariate normal layer using an L1 penalized precision matrix. Methods Preliminaries Let n, p, and s denote
Odd-Char Multivariate Hidden Field Equations Chia-Hsin Owen Chen1
International Association for Cryptologic Research (IACR)
Odd-Char Multivariate Hidden Field Equations Chia-Hsin Owen Chen1 , Ming-Shing Chen1 , Jintai Ding2. Sci., Technische Universität Darmstadt, Germany, jak@cccmz.de Abstract. We present a multivariate ecient and scalable than any other extant multivariate encryption scheme. Switching to odd
The Multivariate Probabilistic Encryption Scheme MQQENC Danilo Gligoroski and Simona Samardjiska
International Association for Cryptologic Research (IACR)
The Multivariate Probabilistic Encryption Scheme MQQENC Danilo Gligoroski and Simona Samardjiska@item.ntnu.no, simonas@item.ntnu.no Abstract. We propose a new multivariate probabilistic encryption schemeSIG, the trapdoor is constructed using quasigroup string transformations with multivariate quadratic quasi groups
International Association for Cryptologic Research (IACR)
Table of Contents A Multivariate Signature Scheme with an almost cyclic public key . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 #12;A Multivariate Signature Scheme with an almost cyclic public key Albrecht Petzoldt1,buchmann}@cdc.informatik.tu-darmstadt.de Abstract. Multivariate public key cryptography is one of the main approaches to guarantee the security
TOT, a Fast Multivariate Public Key Cryptosystem with Basic Secure Trapdoor
International Association for Cryptologic Research (IACR)
TOT, a Fast Multivariate Public Key Cryptosystem with Basic Secure Trapdoor Wuqiang Shen-way trapdoor function, and then propose a new multivariate public key cryptosystem called TOT, which can though C was broken, its high speed has been affirmed). Keywords: TOT; multivariate public key
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
Kockelman, Kara M.
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity By Jianming Ma, and lead to potential biases in sample databases. This paper offers a multivariate Poisson specification severity, Gibbs sampler, Markov chain Monte Carlo (MCMC) simulation, multivariate Poisson regression #12
Image-based multivariate profiling of drug responses from single cells
Cai, Long
Image-based multivariate profiling of drug responses from single cells Lit-Hsin Loo, Lani F Wu are required for summarizing high- throughput, image-based drug screening data. Here we present a multivariate transform distributions of multi- dimensional, phenotypic measurements from single cells into multivariate
Multivariate Population Balances via Moment and Monte Carlo Simulation Methods: An Important Sol application of current/future importance, a multivariate description is required, for which the existing, hopefully, motivate a broader attack on important multivariate population balance problems, including those
SBIM(Q) -a Multivariate Polynomial Trapdoor Function over the Field of Rational Numbers
International Association for Cryptologic Research (IACR)
SBIM(Q) - a Multivariate Polynomial Trapdoor Function over the Field of Rational Numbers Smile.mileva@ugd.edu.mk Abstract. In this paper we define a trapdoor function called SBIM(Q) by using multivariate polynomials over the field of rational numbers Q. The public key consists of 2n multivariate polynomials with 3n variables y1
Multivariate Data Assimilation in the Tropics by Using Equatorial Waves NEDJELJKA ZAGAR
Zagar, Nedjeljka
Multivariate Data Assimilation in the Tropics by Using Equatorial Waves NEDJELJKA ZAGAR 1 multivariate assimila- tion methodology. This applies to both dry and moist idealized tropical systems as well as to a 4D-Var NWP assimilation system. Key words: Tropics, Data assimilation, 4D-Var, Multivariate
International Association for Cryptologic Research (IACR)
Table of Contents A Multivariate Signature Scheme with an almost cyclic public key . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 #12; A Multivariate Signature Scheme with an almost cyclic public key Albrecht Petzoldt 1,buchmann}@cdc.informatik.tudarmstadt.de Abstract. Multivariate public key cryptography is one of the main approaches to guarantee the security
Comparison of Approximation Methods for Computing Tolerance Factors for a Multivariate
Krishnamoorthy, Kalimuthu
Comparison of Approximation Methods for Computing Tolerance Factors for a Multivariate Normal approximation methods for computing the tolerance factors of a multivariate normal population. These approximate on the multivariate setup is rather limited, how- ever. The first attempt at constructing tolerance regions
The Multivariate Probabilistic Encryption Scheme MQQ-ENC Danilo Gligoroski and Simona Samardjiska
International Association for Cryptologic Research (IACR)
The Multivariate Probabilistic Encryption Scheme MQQ-ENC Danilo Gligoroski and Simona Samardjiska@item.ntnu.no, simonas@item.ntnu.no Abstract. We propose a new multivariate probabilistic encryption scheme-SIG, the trapdoor is constructed using quasigroup string transformations with multivariate quadratic quasi- groups
Intersplines: A New Approach to Globally Optimal Multivariate Splines Using Interval
Kearfott, R. Baker
- proximators are the multivariate simplex B-splines. Multivariate simplex B-splines consist of Bernstein basis polynomials that are defined on a ge- ometrical structure called a triangulation. Multivariate simplex B. Secondly, the simplex spline models are parametric models, which allows for effi- cient approximation
Characterizing the Hydrodynamics of Bubbling Fluidized Beds with Multivariate Pressure Measurements
Tennessee, University of
Characterizing the Hydrodynamics of Bubbling Fluidized Beds with Multivariate Pressure Measurements mounted on the walls of a bubbling fluidized bed. Our objective was to identify multivariate dynamic of bubbling fluidized beds with multivariate pressure measurements. 2000 AIChE Annual Meeting (Los Angeles
DNA Mixture Interpretation & Statistical Analysis
of Standards and Technology Gaithersburg, Maryland John M. Butler CIB Forensic Science Center Training Seminar Mixture Workshop This workshop is for analysts, technical reviewers and technical leaders performing) National recommendations of the technical UK DNA working group on mixture interpretation for the NDNAD
Statistical Network Analysis: Models, Issues,
Fienberg, Stephen E.
David M. Blei Stephen E. Fienberg Anna Goldenberg Eric P. Xing Alice X. Zheng #12;#12;Contents Preface University), David M. Blei (Princeton), Stephen E. Fienberg (Carnegie Mellon University), Eric P. Xing