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
2011-01-01
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
2011-01-01
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.
2010-03-01
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...
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.
2004-01-06
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)
2002-01-01
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
2005-10-31
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)
2002-01-01
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.
2004-03-23
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.
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
2005-07-01
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)
2005-01-11
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)
2005-07-26
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.
2004-02-03
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
2015-01-01
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...
Apparatus and system for multivariate spectral analysis
Keenan, Michael R. (Albuquerque, NM); Kotula, Paul G. (Albuquerque, NM)
2003-06-24
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.
Parish, Chad M [ORNL
2011-01-01
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.
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
2011-01-01
spectroscopy coupled with multivariate data analysis. I.Krzanowski WJ: Principles of multivariate analysis: a user’set al. : Combining multivariate analysis and monosaccharide
2011-01-01
spectroscopy coupled with multivariate data analysis. I.Krzanowski WJ: Principles of multivariate analysis: a user’set al. : Combining multivariate analysis and monosaccharide
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
Sex determination of the feral house cat Felis catus using multivariate statistical analyses
Pretoria, University of
c'J \\3 Sex determination of the feral house cat Felis catus using multivariate statistical analyses measurements of feral domestic cats Felis catus by principal component and discriminant function analyses blem arose in a study on the population ecology of thr feral domestic cat, Felis catus, inhabiting
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
Asymptotic Analysis of Multivariate Coherent Risks lih@math.wsu.edu
Li, Haijun
Asymptotic Analysis of Multivariate Coherent Risks Haijun Li lih@math.wsu.edu Washington State Asymptotic Analysis of Multivariate Coherent Risks 7th MDA, 08/10/2010 1 / 22 #12;Outline 1 Coherent Risks Asymptotic Analysis of Tail Conditional Expectations Multivariate Regular Variation Tail Risk of Multivariate
Constantin, Alexandra Elena
2012-01-01
Multivariate survival11 Multivariate serial analysis of glioblastomas 11.1 Data94 11.2 Final multivariate model with significant
De Leeuw, Jan
2012-01-01
and Cumulants from Multivariate Distributions. StatisticsTaylor Expan- sion of a Multivariate Function. International79(3):278–305, 1991. MULTIVARIATE CUMULANTS IN R J. Morton
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
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
Improved permeability prediction using multivariate analysis methods
Xie, Jiang
2009-05-15
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...
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
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
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
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
Klippel, Alexander
Case Study: Design and Assessment of an Enhanced Geographic In- formation System for Exploration of Multivariate Health Statistics Robert M. Edsall Department of Geography Arizona State University robedsall United States, within which are ap- proximately 800 "health service areas" (HSAs), multi- county units
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
Shen, Haipeng
2013-01-01
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
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 DeliciousMathematicsEnergyInterested Parties -Department of Energy MoratoriumMoving
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
Random matrix approach to multivariate categorical data analysis
Patil, Aashay
2015-01-01
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)
1995-01-01
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.
2009-01-01
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
Clegg, Samuel M; Barefield, James E; Wiens, Roger C; Sklute, Elizabeth; Dyare, Melinda D
2008-01-01
Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.
2001, Applied Statistics, 50, 143-154. Nonlinear autoregressive time series with multivariate
Glasbey, Chris
series is proposed to model solar radiation data, by specifying joint marginal distributions at low lags, Multiprocess dynamic linear model, Solar radiation 1 Introduction Knowledge of the statistical characteristics of time series of solar radiation has many uses, one of which is in the design and evaluation of solar
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
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 structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed
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, Urbana, IL 61801 SUN, F.-J. (Department of Plant Biology, 505 South Goodwin Avenue, University unlikely that either genus as currently circumscribed is monophyletic (Downie et al. 2002, Sun 2003, Sun
Long, C.L.
1991-02-01
Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers. 19 refs., 15 figs., 6 tabs.
Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M.; Schmitt, Randal L.
2010-09-01
Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.
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)
2007-10-16
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)
2013-11-15
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)
2008-07-15
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
2011-01-01
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 ...
Searches for Dark Matter at the LHC: A Multivariate Analysis in the Mono-$Z$ Channel
Alexandre Alves; Kuver Sinha
2015-07-29
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
2015-01-01
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
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
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
2005-11-24
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
2013-01-01
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
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
Statistical Hot Channel Analysis for the NBSR
Cuadra A.; Baek J.
2014-05-27
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.
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
The Multi-Isotope Process Monitor: Multivariate Analysis of Gamma Spectra
Orton, Christopher R.; Rutherford, Crystal E.; Fraga, Carlos G.; Schwantes, Jon M.
2011-10-30
The International Atomic Energy Agency (IAEA) has established international safeguards standards for fissionable material at spent fuel reprocessing plants to ensure that significant quantities of nuclear material are not diverted from these facilities. Currently, methods to verify material control and accountancy (MC&A) at these facilities require time-consuming and resource-intensive destructive assay (DA). The time delay between sampling and subsequent DA provides a potential opportunity to divert the material out of the appropriate chemical stream. Leveraging new on-line nondestructive assay (NDA) techniques in conjunction with the traditional and highly precise DA methods may provide a more timely, cost-effective and resource efficient means for MC&A verification at such facilities. Pacific Northwest National Laboratory (PNNL) is developing on-line NDA process monitoring technologies, including the Multi-Isotope Process (MIP) Monitor. The MIP Monitor uses gamma spectroscopy and pattern recognition software to identify off-normal conditions in process streams. Recent efforts have been made to explore the basic limits of using multivariate analysis techniques on gamma-ray spectra. This paper will provide an overview of the methods and report our on-going efforts to develop and demonstrate the technology.
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
Goutami Chattopadhyay; Surajit Chattopadhyay; Rajni Jain
2009-10-28
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.
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
Statistical Analysis of Bus Networks in India
Chatterjee, Atanu; Ramadurai, Gitakrishnan
2015-01-01
Through the past decade the field of network science has established itself as a common ground for the cross-fertilization of exciting inter-disciplinary studies which has motivated researchers to model almost every physical system as an interacting network consisting of nodes and links. Although public transport networks such as airline and railway networks have been extensively studied, the status of bus networks still remains in obscurity. In developing countries like India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer some of the basic questions on its evolution, growth, robustness and resiliency. In this paper, we model the bus networks of major Indian cities as graphs in \\textit{L}-space, and evaluate their various statistical properties using concepts from network science. Our analysis reveals a wide spectrum of network topology with the common underlying feature of small-world property. We observe tha...
Seismic Attribute Analysis Using Higher Order Statistics
Greenidge, Janelle Candice
2009-05-15
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...
* 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
Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring
Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.; Schwantes, Jon M.
2014-01-01
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.
1985-01-01
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.
Multivariate analysis of exhaust emissions from heavy-duty diesel fuels
Sjoegren, M.; Ulf, R.; Li, H.; Westerholm, R. [Stockholm Univ. (Sweden)
1996-01-01
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.
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...
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
Understanding Manufacturing Energy Use Through Statistical Analysis
Kissock, J. K.; Seryak, J.
2004-01-01
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...
Palombo, Giulio
2011-01-01
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
2011-03-28
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.
TREATMENT OF MULTIVARIATE ENVIRONMENTAL AND HEALTH PROBLEMS ASSOCIATED WITH OIL SHALE TECHNOLOGY
Kland, M.J.
2010-01-01
Applic.ation of Multivariate Analysis for Interpretation ofthe Proceedings TREATMENT OF MULTIVARIATE ENVIRONMENTAL ANDENG-48 TREATMENT OF MULTIVARIATE ENVIRONMENTAL AND HEALTH
Statistical Analysis It doesn't have to be a
of alleles detected and the quantitative value of the statistical analysis." Buckleton & Curran (2008 is correctly represented as weak or not presented at all." Buckleton, J. and Curran, J. (2008) A discussion) the RMNE statistic wastes information that should be utilised. #12;Curran and Buckleton (2010) Created 1000
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
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
Probabilistic and Statistical Analysis of Perforated Patterns
Misailovic, Sasa
2011-01-19
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 ...
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
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
Overview of multivariate methods and their application to studies of wildlife habitat
Shugart, H.H. Jr.
1980-01-01
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.
Beadle, Sarah
2014-08-30
. 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...
A statistical analysis of tire tread wear
Sperberg, Ronald Leigh
1965-01-01
S Tea'Ds. . . . . . SS The hyyiieability ef Statistiea1 hna1gsis te the Tive Xn4nntry. . . ' ~. . . '. . . . SS Sane ' ef Ce88tVnQtken o e o':e ?. e ' o e o e Meaning ef the "f" Seexes. Xapertn@ee ef the Reeegaitien ef the ' VnxiahXISISe' ' e' o e...' ehich can enlight ata and 'saoaet nightie On Same nea "Xn'in%ice~ Of "4at yxeeentation. wee data analysis and'pxeeentation technIL@des xxi3. l, of nenessitj, be dev'sloyd if the moxd eoyhisticated technixlues-yx'oynsed in this thesis a'ie m...
A statistical analysis of personnel contaminations in 200 Area facilities
Wagner, M.A.; Stoddard, D.H.
1983-05-18
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.
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
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
Data analysis using the Gnu R system for statistical computation
Simone, James; /Fermilab
2011-07-01
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.
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
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
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 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
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
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
Statistical Analysis of X-ray Speckle at the NSLS
Ophelia K. C. Tsui; S. G. J. Mochrie; L. E. Berman
1997-09-30
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.
2013-04-16
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.
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
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
2011-11-18
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.
Principal Components Analysis for Binary Data
Lee, Seokho
2010-07-14
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...
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
HistFitter software framework for statistical data analysis
M. Baak; G. J. Besjes; D. Cote; A. Koutsman; J. Lorenz; D. Short
2014-10-06
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.
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 ...
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)
2012-04-30
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.
Statistical Analysis of Abnormal Electric Power Grid Behavior
Ferryman, Thomas A.; Amidan, Brett G.
2010-10-30
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.
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
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
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
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
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
Statistical analysis of cascading failures in power grids
Chertkov, Michael; Pfitzner, Rene; Turitsyn, Konstantin
2010-12-01
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
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
Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.; Casella, Amanda J.; Peterman, Dean; Bryan, Samuel A.
2013-11-05
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
2013-12-01
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.
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
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
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
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
VOStat: A Statistical Web Service for Astronomers
Chakraborty, Arnab; Babu, G Jogesh
2013-01-01
VOStat is a Web service providing interactive statistical analysis of astronomical tabular datasets. It is integrated into the suite of analysis and visualization tools associated with the international Virtual Observatory (VO) through the SAMP communication system. A user supplies VOStat with a dataset extracted from the VO, or otherwise acquired, and chooses among $\\sim 60$ statistical functions. These include data transformations, plots and summaries, density estimation, one- and two-sample hypothesis tests, global and local regressions, multivariate analysis and clustering, spatial analysis, directional statistics, survival analysis (for censored data like upper limits), and time series analysis. The statistical operations are performed using the public domain {\\bf R} statistical software environment, including a small fraction of its $>4000$ {\\bf CRAN} add-on packages. The purpose of VOStat is to facilitate a wider range of statistical analyses than are commonly used in astronomy, and to promote use of m...
Statistical Analysis of Tank 5 Floor Sample Results
Shine, E. P.
2013-01-31
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.
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
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
Plutonium metal exchange program : current status and statistical analysis
Tandon, L.; Eglin, J. L.; Michalak, S. E.; Picard, R. R.; Temer, D. J.
2004-01-01
The Rocky Flats Plutonium (Pu) Metal Sample Exchange program was conducted to insure the quality and intercomparability of measurements such as Pu assay, Pu isotopics, and impurity analyses. The Rocky Flats program was discontinued in 1989 after more than 30 years. In 2001, Los Alamos National Laboratory (LANL) reestablished the Pu Metal Exchange program. In addition to the Atomic Weapons Establishment (AWE) at Aldermaston, six Department of Energy (DOE) facilities Argonne East, Argonne West, Livermore, Los Alamos, New Brunswick Laboratory, and Savannah River are currently participating in the program. Plutonium metal samples are prepared and distributed to the sites for destructive measurements to determine elemental concentration, isotopic abundance, and both metallic and nonmetallic impurity levels. The program provides independent verification of analytical measurement capabilies for each participating facility and allows problems in analytical methods to be identified. The current status of the program will be discussed with emphasis on the unique statistical analysis and modeling of the data developed for the program. The discussion includes the definition of the consensus values for each analyte (in the presence and absence of anomalous values and/or censored values), and interesting features of the data and the results.
Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint
Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad
2015-12-08
Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.
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
Parallel and Statistical Analysis and Modeling of Nanometer VLSI Systems
Liu, Xue-Xin
2013-01-01
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.
Stochastic precipitation generation based on a multivariate autoregression model
Borchers, Brian
Stochastic precipitation generation based on a multivariate autoregression model Oleg V. Makhnin of stochastic precipitation generation has long been of interest. A good generator should produce time series with statistical properties to match those of the real precipitation. Here, we present a multivariate
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
The Speedup-Test: A Statistical Methodology for Program Speedup Analysis and Computation
Paris-Sud XI, Université de
The Speedup-Test: A Statistical Methodology for Program Speedup Analysis and Computation Sidi presents a rigorous statistical methodology regarding program performance analysis. We rely on well known is implemented and distributed as an open source tool based on R software. keywords: Program performance
Chen, Brian Y.
VASP-S: A Volumetric Analysis and Statistical Model for Predicting Steric Influences on Protein, are harder to find. To assist this process, we present VASP-S (Volumetric Analysis of Surface Properties with Statistics), an unsupervised volumetric analysis and statistical model for isolating statistically
Statistical analysis and transfer of coarse-grain pictorial style
Bae, Soonmin
2005-01-01
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 ...
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
2010-12-23
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.
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
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
Statistical analysis of motion contrast in optical coherence tomography angiography
Cheng, Yuxuan; Pan, Cong; Lu, Tongtong; Hong, Tianyu; Ding, Zhihua; Li, Peng
2015-01-01
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.
UNDERSTANDING MANUFACTURING ENERGY USE THROUGH STATISTICAL ANALYSIS KELLY KISSOCK AND JOHN SERYAK
Kissock, Kelly
UNDERSTANDING MANUFACTURING ENERGY USE THROUGH STATISTICAL ANALYSIS KELLY KISSOCK AND JOHN SERYAK, OHIO ABSTRACT Energy in manufacturing facilities is used for direct production of goods, space for statistically analyzing plant energy use in terms of these major end uses. The methodology uses as few as 60
Fazzio, Thomas J. (Thomas Joseph)
2010-01-01
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 ...
Statistical analysis of illiquidity risk and premium in financial price signals
Khandani, Amir E. (Amir Ehsan), 1979-
2009-01-01
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 ...
Understanding the performance of broadband networks through the statistical analysis of speed tests
García García, Rubén, S.M. Massachusetts Institute of Technology
2011-01-01
In this thesis we performed the statistical analysis of a dataset containing raw data captured through user-initiated Internet speed tests. The NDT dataset represents the largest publicly available source of raw measurements ...
Internet Data Analysis for the Undergraduate Statistics Curriculum
Juana Sanchez; Yan He
2011-01-01
Haythornthwaite edts. (2002). The Internet in Everyday Life.Where Mathematics meets the Internet. Notices of the AMS,Internet Data Analysis for the Undergraduate Statitics
Statistical and risk analysis for the measured and predicted axial response of 100 piles
Perdomo, Dario
1986-01-01
. , 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...
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
2012-06-07
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.
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
2013-04-01
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.
Defect site prediction based upon statistical analysis of fault signatures
Trinka, Michael Robert
2004-09-30
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...
Multivariate Forecast Evaluation And Rationality Testing
Komunjer, Ivana; OWYANG, MICHAEL
2007-01-01
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
2008-01-01
79 2.2.1 Multivariate Normal74 2.2 Multivariate Contours and Autocontours . . . . . . .43 x CHAPTER II: MULTIVARIATE AUTOCONTOURS FOR SPECIFICATION
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
Templates and Examples — Statistics and Search Log Analysis
Broader source: Energy.gov [DOE]
Here you will find custom templates and EERE-specific examples you can use to plan, conduct, and report on your usability and analysis activities. These templates are examples of forms you might use, but you are not required to use them for EERE products.
Statistical language analysis for automatic exfiltration event detection.
Robinson, David Gerald
2010-04-01
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.
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
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
2006-01-01
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
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
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
Analysis of compressive fracture in rock using statistical techniques
Blair, S.C.
1994-12-01
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 Skew-t Distributions in Econometrics and Environmetrics
Marchenko, Yulia V.
2012-02-14
-1 MULTIVARIATE SKEW-T DISTRIBUTIONS IN ECONOMETRICS AND ENVIRONMETRICS A Dissertation by YULIA V. MARCHENKO Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR... OF PHILOSOPHY December 2010 Major Subject: Statistics MULTIVARIATE SKEW-T DISTRIBUTIONS IN ECONOMETRICS AND ENVIRONMETRICS A Dissertation by YULIA V. MARCHENKO Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment...
Balasubramanian, Vijay
2007-04-25
variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate statistical technique known as principal component analysis...
The statistical analysis techniques to support the NGNP fuel performance experiments
Binh T. Pham; Jeffrey J. Einerson
2013-10-01
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 Approaches to Classification in Extragalactic Astronomy
Fraix-Burnet, Didier; Chattopadhyay, Asis Kumar
2015-01-01
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.
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
Multivariate characterization of hydrogen Balmer emission in cataclysmic variables
Gordon E. Sarty; Kinwah Wu
2006-08-18
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.
Introduction to Markov Chain Monte Carlo Simulations and their Statistical Analysis
Bernd A. Berg
2004-10-19
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.
Stout, Quentin F.
2008-01-01
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
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
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
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
Statistical analysis of illness death processes and semi-competing risks data by
Jin, Jiashun
Statistical analysis of illness death processes and semi-competing risks data by Jinfeng Xu-competing risks data frequently arise in clinical and observational studies where the subject can experience is similar to, but under weaker assump- tions than that of the latent failure times in competing risks data
Statistical analysis of electric power production costs JORGE VALENZUELA and MAINAK MAZUMDAR*
Mazumdar, Mainak
Statistical 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 uncertainty in the forecast of production costs. 1. Introduction One of the characteristics of electric power
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 December 2010 Keywords: Wind Wind speed Energy Capacity factor Electricity Chile a b s t r a c t Bearing role in any future national energy generation matrix. With a view to understanding the local wind
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
Multiple Oscillatory Modes of the Argentine Basin. Part I: Statistical Analysis WILBERT WEIJER
Weijer, Wilbert
Multiple Oscillatory Modes of the Argentine Basin. Part I: Statistical Analysis WILBERT WEIJER NumÃ©riques, Paris, France SARAH T. GILLE Scripps Institution of Oceanography, La Jolla, California HENK A surface height in the Argentine Basin indicate that strong variability occurs on a time scale of 20 30
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
2007-01-01
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.
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
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
STATISTICAL ANALYSIS OF CURRENT SHEETS IN THREE-DIMENSIONAL MAGNETOHYDRODYNAMIC TURBULENCE
Zhdankin, Vladimir; Boldyrev, Stanislav; Uzdensky, Dmitri A.; Perez, Jean C. E-mail: boldyrev@wisc.edu E-mail: jcperez@wisc.edu
2013-07-10
We develop a framework for studying the statistical properties of current sheets in numerical simulations of magnetohydrodynamic (MHD) turbulence with a strong guide field, as modeled by reduced MHD. We describe an algorithm that identifies current sheets in a simulation snapshot and then determines their geometrical properties (including length, width, and thickness) and intensities (peak current density and total energy dissipation rate). We then apply this procedure to simulations of reduced MHD and perform a statistical analysis on the obtained population of current sheets. We evaluate the role of reconnection by separately studying the populations of current sheets which contain magnetic X-points and those which do not. We find that the statistical properties of the two populations are different in general. We compare the scaling of these properties to phenomenological predictions obtained for the inertial range of MHD turbulence. Finally, we test whether the reconnecting current sheets are consistent with the Sweet-Parker model.
The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments
Bihn T. Pham; Jeffrey J. Einerson
2010-06-01
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.
Statistical analysis of lateral migration of the Rio Grande, Gigi A. Richarda,T, Pierre Y. Julienb
Julien, Pierre Y.
Statistical analysis of lateral migration of the Rio Grande, New Mexico Gigi A. Richarda,T, Pierre migration rates of alluvial rivers are affected by changes in water and sediment regimes. The Rio Grande, and water and sediment regimes. A statistical analysis reveals that migration rates primarily decreased
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
Applied Mathematics BS, Statistics Emphasis, 2015-2016 Name ID# Date
Barrash, Warren
Applied Mathematics BS, Statistics Emphasis, 2015-2016 Name ID# Date Course Number and Title Mathematics I 3 MATH 275 Multivariable and Vector Calculus 4 CID MATH 287 Communication in the Mathematical To Computational Mathematics 3 FF MATH 401 Senior Thesis in the Mathematical Sciences 1 MATH 465 Numerical Analysis
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)
1996-10-01
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.
A Complete Statistical Analysis for the Quadrupole Amplitude in an Ellipsoidal Universe
Alessandro Gruppuso
2007-05-17
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.
A Statistical Analysis of Santa Barbara Ambulance Response in 2006: Performance Under Load
Chang, Joshua C; Schoenberg, Frederic P.
2009-01-01
The R Language and Environment for Statistical Computing 17R Development Core Team. R: A Language and Environment for Statistical
Statistical analysis of multipole components in the magnetic field of the RHIC arc regions
Beebe-Wang,J.; Jain, A.
2009-05-04
The existence of multipolar components in the dipole and quadrupole magnets is one of the factors limiting the beam stability in the RHIC operations. Therefore, the statistical properties of the non-linear fields are crucial for understanding the beam behavior and for achieving the superior performance in RHIC. In an earlier work [1], the field quality analysis of the RHIC interaction regions (IR) was presented. Furthermore, a procedure for developing non-linear IR models constructed from measured multipolar data of RHIC IR magnets was described. However, the field quality in the regions outside of the RHIC IR had not yet been addressed. In this paper, we present the statistical analysis of multipolar components in the magnetic fields of the RHIC arc regions. The emphasis is on the lower order components, especially the sextupole in the arc dipole and the 12-pole in the quadrupole magnets, since they are shown to have the strongest effects on the beam stability. Finally, the inclusion of the measured multipolar components data of RHIC arc regions and their statistical properties into tracking models is discussed.
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/
Statistical analysis and modeling of intermittent transport events in the tokamak SOL
Anderson, J; Xanthopoulos, P; Ricci, P; Furno, I
2014-01-01
The turbulence observed in the scrape-off-layer of a tokamak is often characterized by intermittent events of bursty nature, a feature which raises concerns about the prediction of heat loads on the physical boundaries of the device. It appears thus necessary to delve into the statistical properties of turbulent physical fields such as density, electrostatic potential and temperature, focusing on the mathematical expression of tails of the probability distribution functions. The method followed here is to generate statistical information from time-traces of the plasma density stemming from Braginskii-type fluid simulations, and check this against a first-principles theoretical model. The analysis of the numerical simulations indicates that the probability distribution function of the intermittent process contains strong exponential tails, as predicted by the analytical theory.
Multivariate Visual Representations 2
Stasko, John T.
Unkown Dust & Magnet · Altogether different metaphor · Data cases represented as small bits of iron dust 7450 9 Interaction · Iron bits (data) are drawn toward magents (attributes) proportional to that data analysis Data mining Knowledge discovery Compu
van Dorp, Johan René
1999-01-01
Int. J. Production Economics 58 (1999) 17--29 Statistical dependence in risk analysis for project of the methodology are described along with an example of project risk analysis in a manufacturing domain correlation; Engineering judgment 1. Introduction Risk analysis on project networks is defined here
Applications of Minkowski Functionals to the Statistical Analysis of Dark Matter Models
Michael Platzoeder; Thomas Buchert
1995-09-04
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.
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
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
Multivariate discriminant and iterated resultant
Jingjun Han
2015-07-22
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 $f(x,y,z)$.
In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units
Ranjan, Niloo [ORNL; Sanyal, Jibonananda [ORNL; New, Joshua Ryan [ORNL
2013-08-01
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
2008-11-14
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.
Jagadheep D. Pandian; Paul F. Goldsmith
2007-08-23
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.
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
Statistical analysis of content of Cs-137 in soils in Bansko-Razlog region
Kobilarov, R. G.
2014-11-18
Statistical analysis of the data set consisting of the activity concentrations of {sup 137}Cs in soils in Bansko–Razlog region is carried out in order to establish the dependence of the deposition and the migration of {sup 137}Cs on the soil type. The descriptive statistics and the test of normality show that the data set have not normal distribution. Positively skewed distribution and possible outlying values of the activity of {sup 137}Cs in soils were observed. After reduction of the effects of outliers, the data set is divided into two parts, depending on the soil type. Test of normality of the two new data sets shows that they have a normal distribution. Ordinary kriging technique is used to characterize the spatial distribution of the activity of {sup 137}Cs over an area covering 40 km{sup 2} (whole Razlog valley). The result (a map of the spatial distribution of the activity concentration of {sup 137}Cs) can be used as a reference point for future studies on the assessment of radiological risk to the population and the erosion of soils in the study area.
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
2011-01-01
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.
2011-01-01
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; Fout, Nathaniel; Ma, Kwan - Liu
2010-01-01
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 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
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
Lilly, Jonathan
2012-01-01
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
DWPF Sample Vial Insert Study-Statistical Analysis of DWPF Mock-Up Test Data
Harris, S.P. [Westinghouse Savannah River Company, AIKEN, SC (United States)
1997-09-18
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.
Lee, Ho Young
2000-01-01
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...
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
A Multivariate Statistical Approach to Spatial Representation of
Vermont, University of
) and possible remediation systems that last 30 years or more after site closure. Landfills are the primary, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands Managers of landfill sites and genome-based data) from a landfill leachate contaminated aquifer in Banisveld, The Netherlands
Neurogenomics in the mouse model : multivariate statistical methods and analyses
Zapala, Matthew Alan
2007-01-01
P -value proportion of variation in pair-wiseP -value proportion of variation explained abovevalue proportion of variation explained = 0.35;
Neurogenomics in the mouse model : multivariate statistical methods and analyses
Zapala, Matthew Alan
2007-01-01
development of the rat hypothalamus. Adv Anat Embryol Cellmidbrain, excluding hypothalamus (DiE-MD), entorhinalformation (HiF), hypothalamus (Hy), inferior colliculus (
Wolfrum, E. J.; Sluiter, A. D.
2009-01-01
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.
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
Brown, Michael E.
to appear in Astrophysical Journal (Letters) An Analysis of the Statistics of the HST Kuiper Belt Belt objects in the HST data of Cochran et al. (1995), in which they report the discovery al. The detection of such a population of HalleyÂsized Kuiper Belt objects with these data
Yu, Victoria; Kishan, Amar U.; Cao, Minsong; Low, Daniel; Lee, Percy; Ruan, Dan
2014-03-15
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.
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.
2009-01-01
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
Of Disasters and Dragon Kings: A Statistical Analysis of Nuclear Power Incidents & Accidents
Wheatley, Spencer; Sornette, Didier
2015-01-01
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...
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.
2009-01-01
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
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
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
Reddy, T. A.; Claridge, D.; Wu, J.
1992-01-01
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...
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
A multivariable Chinese remainder theorem Oliver Knill
Knill, Oliver
A multivariable Chinese remainder theorem Oliver Knill January 27, 2005 Abstract In this note we show a multivariable version of the Chinese remainder theorem: a system of linear modular equations ai1 in a parallelepiped of volume M = m1m2 · · · mn. The Chinese remainder theorem is the special case, where A has only
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 p.1/25 #12;NTNU used in animal and plant breeding. In this study: Wild life population Several traits simultaneously
Multivariate orthogonal polynomial and integrable systems
Gerardo Ariznabarreta; Manuel Mañas
2015-03-05
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.
Computational Statistics & Data Analysis 51 (2007) 63806394 www.elsevier.com/locate/csda
Liu, Yufeng
2007-01-01
; accepted 3 February 2007 Available online 20 February 2007 Abstract The standard support vector machine vector machines with adaptive Lq penalty Yufeng Liua, , Hao Helen Zhangb , Cheolwoo Parkc , Jeongyoun Ahnc aDepartment of Statistics and Operations Research, Carolina Center for Genome Sciences, University
Widen, Joakim; Waeckelgaard, Ewa; Paatero, Jukka; Lund, Peter
2010-03-15
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)
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
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 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 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 GOURAB MUKHERJEE Department of Statistics Stanford directions in statistical probability forecasting. Building on these parallels we present a frequentist
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
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)
2014-06-01
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 emulation of a tsunami model for sensitivity analysis and uncertainty quantification
Sarri, A; Dias, F
2012-01-01
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...
Absolute Continuous Multivariate Generalized Exponential Distribution
Kundu, Debasis
Absolute Continuous Multivariate Generalized Exponential Distribution Debasis Kundu1,2 & Ankush Kumar1 & Arjun K. Gupta3 Abstract Generalized exponential distribution has received some attention continuous bivariate generalized exponential distribution. In this paper we propose an absolute continuous
The University of Chicago Department of Statistics
the analysis of a 24- dimensional Australian electricity spot prices. Some key words: Lasso; Cross a robust procedure for constructing a sparse estimator of a multivariate re- gression coefficient matrix iteratively where at each EM iteration suitably modified multivariate regression with covari- ance estimation
Guo, Genliang; George, S.A.; Lindsey, R.P.
1997-08-01
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.
Crow, Ben D
2006-01-01
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...
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
Development of a thermobalance and analysis of lignites by thermogravimetric and statistical methods
Ferguson, James Allen
1984-01-01
LIST OF TABLES LIST OF FIGURES INTRODUCTION V11 V1 1 1 THERMAL ANALYSIS COAL EXPERIMENTAL METHODS 12 20 CALCULATING THE CALORIFIC VALUE OF LIGNITES FROM PROXIMATE ANALYSIS DATA GAUSS REDUCTION ILLUSTRATION CONSTRUCTION OF THE THERMOBALANCE... diagram illustrating procedure for determining the proximate analyses of coal or lignite by thermogravimetry 20 EXPERIMENTAL METHODS CALCULATING THE CALORIFIC VALUE OF LIGNITES FROM PROXIMATE ANALISIS DATA One of the most important properties of a...
Testing Multivariate Linear Functions: Overcoming the Generator Bottleneck
Ergun, Funda
Testing Multivariate Linear Functions: Overcoming the Generator Bottleneck Funda ErgÂ¨un \\Lambda present efficient methods for selfÂtesting multivariate linear functions. We then apply these methods the problem of selfÂtesting multivariate linear functions, i.e., given a multivariate linear function f
, 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
Rebound 2007: Analysis of U.S. Light-Duty Vehicle Travel Statistics
Greene, David L
2010-01-01
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
2015-01-01
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.
Extracting bb Higgs Decay Signals using Multivariate Techniques
Smith, W Clarke; /George Washington U. /SLAC
2012-08-28
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.
Statistical static timing analysis considering the impact of power supply noise in VLSI circuits
Kim, Hyun Sung
2009-06-02
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...
Dr. Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson
2012-10-01
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.
A Model of the Statistical Power of Comparative Genome Sequence Analysis
Batzoglou, Serafim
by their evolutionary conservation [1,2,3]. It will be instrumental for achieving the goal of the Human Genome Project to comprehensively identify functional elements in the human genome [4]. How many comparative genome sequences do we not contribute significant information to human genome analysis? Since sequencing is expensive and capacity
Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago
O'Brien, Timothy E.
) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and Schmeling 2006, 2007; Shen et al. 2009). The composition of aerosols is region-specific and encompasses inorganic and organic species of natural and anthropogenic ori- gin, present due to primary emission
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
Analysis of hyper-spectral data derived from an imaging Fourier transform: A statistical perspective
Sengupta, S.K.; Clark, G.A.; Fields, D.J.
1996-01-10
Fourier transform spectrometers (FTS) using optical sensors are increasingly being used in various branches of science. Typically, a FTS generates a three-dimensional data cube with two spatial dimensions and one frequency/wavelength dimension. The number of frequency dimensions in such data cubes is generally very large, often in the hundreds, making data analytical procedures extremely complex. In the present report, the problem is viewed from a statistical perspective. A set of procedures based on the high degree of inter-channel correlation structure often present in such hyper-spectral data, has been identified and applied to an example data set of dimension 100 x 128 x 128 comprising 128 spectral bands. It is shown that in this case, the special eigen-structure of the correlation matrix has allowed the authors to extract just a few linear combinations of the channels (the significant principal vectors) that effectively contain almost all of the spectral information contained in the data set analyzed. This in turn, enables them to segment the objects in the given spatial frame using, in a parsimonious yet highly effective way, most of the information contained in the data set.
BS in STATISTICS: Statistical Science Emphasis (695220) MAP Sheet Department of Statistics
Dahl, David B.
: Statistics Stat 497R Introduction to Statistical Research Stat 538 Survival Analysis Note: Students may countBS in STATISTICS: Statistical Science Emphasis (695220) MAP Sheet Department of Statistics & Oral Communication Quantitative Reasoning Languages of Learning (Math or Language) Arts, Letters
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
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
Statistical Analysis of Historical State-Level Residential Energy Consumption Trends
Belzer, David B.; Cort, Katherine A.
2004-08-01
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
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
Development of Statistical Energy Analysis Tools for Toyota Motor Engineering & Manufacturing
Chen, J; Collins, Ro.; Gao, G.; Schaffer, D.; Wu, J.
2014-01-01
? Recommendations ESL-IE-14-05-06 Proceedings of the Thrity-Sixth Industrial Energy Technology Conference New Orleans, LA. May 20-23, 2014 Duke Team Project Overview ? Client: Tim Hertel, Toyota Energy Engineer and Energy Manager ? Visited Kentucky plant to learn...-Sixth Industrial Energy Technology Conference New Orleans, LA. May 20-23, 2014 Presentation Agenda ? Project introduction and goals ? Duke team’s energy consumption models ? Analysis of Toyota’s current consumption model ? Duke vs. Toyota’s model ? Results...
High Statistics Analysis using Anisotropic Clover Lattices: (II) Three-Baryon Systems
Silas R. Beane; William Detmold; Thomas C Luu; Kostas Orginos; Assumpta Parreno; Martin J. Savage; Aaron Torok; Andre Walker-Loud
2009-05-04
We present the results of an exploratory Lattice QCD calculation of three-baryon systems through a high-statistics study of one ensemble of anisotropic clover gauge-field configurations with a pion mass of m_\\pi ~ 390 MeV. Because of the computational cost of the necessary contractions, we focus on correlation functions generated by interpolating-operators with the quantum numbers of the $\\Xi^0\\Xi^0 n$ system, one of the least demanding three baryon systems in terms of the number of contractions. We find that the ground state of this system has an energy of E_{\\Xi^0\\Xi^0n}= 3877.9\\pm 6.9\\pm 9.2\\pm3.3 MeV corresponding to an energy-shift due to interactions of \\delta E_{\\Xi^0\\Xi^0n}=E_{\\Xi^0\\Xi^0n}-2M_{\\Xi^0} -M_n=4.6\\pm 5.0\\pm 7.9\\pm 4.2 MeV. There are a significant number of time-slices in the three-baryon correlation function for which the signal-to-noise ratio is only slowly degrading with time. This is in contrast to the exponential degradation of the signal-to-noise ratio that is observed at larger times, and is due to the suppressed overlap of the source and sink interpolating-operators that are associated with the variance of the three-baryon correlation function onto the lightest eigenstates in the lattice volume (mesonic systems). As one of the motivations for this area of exploration is the calculation of the structure and reactions of light nuclei, we also present initial results for a system with the quantum numbers of the triton (pnn). This present work establishes a path to multi-baryon systems, and shows that Lattice QCD calculations of the properties and interactions of systems containing four and five baryons are now within sight.
Statistical analysis of liquid seepage in partially saturated heterogeneous fracture systems
Liou, T.S.
1999-12-01
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.
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
A MULTIVARIABLE CHINESE REMAINDER THEOREM OLIVER KNILL
Knill, Oliver
A MULTIVARIABLE CHINESE REMAINDER THEOREM OLIVER KNILL Abstract. Using an adaptation of Qin Jiushao and in each row, at least one matrix element aij is relatively prime to mi. The Chinese remainder theorem,01A25,15A06. Key words and phrases. Chinese Remainder Theorem, History of number theory, Linear Dio
Multivariate Distributions with Proportional Reversed Hazard Marginals
Kundu, Debasis
Multivariate Distributions with Proportional Reversed Hazard Marginals Debasis Kundu1 & Manuel Franco2 & Juana-Maria Vivo3 Abstract Several univariate proportional reversed hazard models have been a class of bivariate models with proportional reversed hazard marginals. It is observed that the proposed
Research Article Multivariate Interpolation of Precipitation
Mitasova, Helena
Research Article Multivariate Interpolation of Precipitation Using Regularized Spline with Tension to interpolate daily and annual mean precipitation in regions with complex terrain. Tension, smoothing the spatial model of precipitation in terms of its predictive error, spatial pattern and water balance. 1
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
2014-01-01
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.
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 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 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
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
N. Panja; A. K. Chattopadhyay
2014-12-05
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.
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.
2013-03-01
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.
Comnes, G.A.; Belden, T.N.; Kahn, E.P.
1995-02-01
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.
Experimental Statistics NBS Handbook 91: Experimental Statistics [1] was
Experimental Statistics NBS Handbook 91: Experimental Statistics [1] was first published in 1963 as a series of five Army Ordnance Pamphlets OSRDDP 20-110-114. The publication was prepared in the Statistical. Basic Statistical Concepts and Analysis and Inter- pretation of Measurement Data 2. Standard Techniques
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
2011-06-21
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
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
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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield MunicipalTechnicalInformation4563Abuse Tolerance(Conference) |stabilized by(Conference) | SciTech
MARGINALIZED TRANSITION RANDOM EFFECTS MODELS FOR MULTIVARIATE
Daniels, Michael J.
, Ankara, Turkey email: oilk@metu.edu.tr 2 Department of Statistics, University of Florida, Gainesville, FL
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
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)
2009-05-15
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.
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 QA:QA J-E-1 SECTION J APPENDIX E LIST OF APPLICABLE DIRECTIVESDepartmentSpecialCodetheDeliveryEnergy States
Independent Statistics & Analysis
Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1Markets See full Hydrocarbon7,747Petroleum-'VApril
Independent Statistics & Analysis
Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1Markets See full
Independent Statistics & Analysis
Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry Natural GasNatural GasEIA lowerslong4,Guide toHighHow
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 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 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
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
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
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...
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
Method for factor analysis of GC/MS data
Van Benthem, Mark H; Kotula, Paul G; Keenan, Michael R
2012-09-11
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.
Multivariate orthogonal Laurent polynomials and integrable systems
Gerardo Ariznabarreta; Manuel Mañas
2015-06-30
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.
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
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
Paul, Satyakama; Marwala, Tshilidzi
2011-01-01
South Africa assumes a significant position in the insurance landscape of Africa. The present research based upon qualitative and quantitative analysis, shows that it shows the characteristics of a Complex Adaptive System. In addition, a statistical analysis of risk measures through Value at risk and Conditional tail expectation is carried out to show how an individual insurance company copes under external complexities. The authors believe that an explanation of the coping strategies, and the subsequent managerial implications would enrich our understanding of complexity in business.
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01
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
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
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
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
DIRECTIONS AND FACTORIZATIONS OF ZEROS AND POLES IN MULTIVARIABLE SYSTEMS
Skogestad, Sigurd
DIRECTIONS AND FACTORIZATIONS OF ZEROS AND POLES IN MULTIVARIABLE SYSTEMS K. Havre 1 and S. INTERNAL REPORT June 96. Abstract. Directionality of zeros and poles in multivariable systems izations of RHPzeros and poles in Blaschke products, with statespace realizations dependent on the pole
DIRECTIONS AND FACTORIZATIONS OF ZEROS AND POLES IN MULTIVARIABLE SYSTEMS
Skogestad, Sigurd
DIRECTIONS AND FACTORIZATIONS OF ZEROS AND POLES IN MULTIVARIABLE SYSTEMS K. Havre1 and S. INTERNAL REPORT - June 96. Abstract. Directionality of zeros and poles in multivariable systems- izations of RHP-zeros and poles in Blaschke products, with state-space realizations dependent on the pole
Michele Arzano; Dario Benedetti
2008-09-04
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.
Chang, Wen-Kuei; Hong, Tianzhen
2013-01-01
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.
Multivariate volume visualization through dynamic projections
Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer -Timo; Pascucci, Valerio
2014-11-01
We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Multivariate Central Limit Theorem in Quantum Dynamics
Simon Buchholz; Chiara Saffirio; Benjamin Schlein
2013-09-06
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).
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
Residential solar home resale analysis
Noll, S.A.
1980-01-01
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.
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01
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
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))
1993-01-01
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
2009-09-16
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.
Arrowood, L.F.; Tonn, B.E.
1992-02-01
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.
A method of Weil sum in multivariate quadratic cryptosystem
Harayama, Tomohiro
2007-09-17
A new cryptanalytic application is proposed for a number theoretic tool Weil sum to the birthday attack against multivariate quadratic trapdoor function. This new customization of the birthday attack is developed by ...
Multi-variable optimization of pressurized oxy-coal combustion
Zebian, Hussam
2011-01-01
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 ...
Synthesis of reduced order prefilters for multivariable tracking
Bement, Matthew Thomas
1997-01-01
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
2012-02-14
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...
Chapter 40: Multivariate autoregressive models W. Penny and L. Harrison
Penny, Will
Chapter 40: Multivariate autoregressive models W. Penny and L. Harrison April 28, 2006 Introduction and parameter estimation which is introduced in Chapter 24 and is described fully in [Penny and Roberts 2002
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
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
Jan de Leeuw
2011-01-01
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
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
Multivariate analysis of functional metagenomes *Elizabeth A. Dinsdale1
Ponomarenko, Vadim
, the Human Microbiome Project has expanded our understanding of the microbes inhabiting our own bodies that could be used to compare and contrast the metabolic functions of microbes (or viruses) within. Genes that are contributed by phage are a key contributors to the success of microbes invasion
A Multivariate Analysis of Freeway Speed and Headway Data
Zou, Yajie
2013-11-11
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 ...
Default Bayesian Analysis for Multivariate Generalized CAR Models
Dass, Sarat C.
to the distribution of federal and state aid based on socio-economic indicators. Health disparity studies analyze how available in this framework where prior selection plays an important role in the infer- ence. The present;1 Introduction Many spatial problems, particularly those concerning environmental, health and socio-economic
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
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
Bill Jackson; Aldo Procacci; Alan D. Sokal
2014-12-02
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.
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; Singh, Sukhdeep; Mandelbaum, Rachel; Matteo, Tiziana Di; Feng, Yu; Khandai, Nishikanta
2015-03-11
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
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
A multivariate phase distribution and its estimation
Charles F. Cadieu; Kilian Koepsell
2009-06-21
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.
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
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
Schmidt, Volker
bodies, and fuel cell technology. Materials produced using different composition and fabrication an important role. The applications of fibre-based materials include thermal insulation, aircraft and car
J. Mark Heinzle; Claes Uggla
2012-12-21
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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home RoomPreservationBio-Inspired SolarAbout /Two0 - 19PortalStatus UpdatesUsage Statistics Usage
Environment/Health/Safety (EHS): Monthly Accident Statistics
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
Personal Protective Equipment (PPE) Injury Review & Analysis Worker Safety and Health Program: PUB-3851 Monthly Accident Statistics Latest Accident Statistics Accident...
Parallel auto-correlative statistics with VTK.
Pebay, Philippe Pierre; Bennett, Janine Camille
2013-08-01
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.
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
Demkin, V. P.; Mel'nichuk, S. V.
2014-09-15
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.
Generalizations of quantum statistics
O. W. Greenberg
2008-05-02
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
2007-01-01
W. , (2007) “The Introductory Statistics Course: A PtolemaicTechnology Innovations in Statistics Education,1, Article 1.and Introductory Statistics Daniel T. Kaplan Macalester
Statistical Software - Overview
de Leeuw, Jan
2010-01-01
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
Jan De Leeuw
2011-01-01
Review of Statistical Software. International As- sociationStatistical Methods Need Software: A View of Statisti- calJournal of Statistical Software, 13, 2004. URL http://www.
David, Mathieu; Garde, Francois; Boyer, Harry
2014-01-01
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...
A multivariate quadrature based moment method for supersonic combustion modeling
Raman, Venkat
QMOM is then used for studying an experimental Mach 2.2 supersonic cavity based combustor. Development of predictiveA multivariate quadrature based moment method for supersonic combustion modeling Pratik Donde models for supersonic combustion is a critical step in design and development of scramjet engines
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)
1997-01-01
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.
Multivariate and Supervised Approaches for Mathematical Morphology in Remote Sensing
Lefèvre, Sébastien
Multivariate and Supervised Approaches for Mathematical Morphology in Remote Sensing S´ebastien Lef`evre Image Sciences, Computer Sciences and Remote Sensing Laboratory (LSIIT) Models, Image and Vision Team MM Supervised MM Applications in Remote Sensing Conclusion Mathematical Morphology is a powerful
ess5011 Robert J. Serfling MULTIVARIATE SYMMETRY AND
Serfling, Robert
, there are many variations on the theme. One can seek to define useful classes of distributions that extend with covariance matrices of form 2 Id but also, for example, certain cases of standard multivariate t and logistic distributions (see Ref. 34, p. 34 and 573). In particular, the standard d-variate t-distribution with m degrees
Multivariate Non-Normality in the WMAP 1st Year Data
Patrick Dineen; Peter Coles
2005-11-29
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.
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
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
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
New likelihoods for shape analysis
Fichet, Sylvain
2014-01-01
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...
Mathematics/Statistics College of Science MATH-BS Code-MASI ...
Susan Kaye Aufderheide
2013-05-15
STAT 35000 Introduction To Statistics (satisfies Statistics Requirement). (3). MA 34100 Foundations Of Analysis or MA 44000 Real Analysis Honors. (3).
SHARE: Statistical Hadronization with Resonances
Giorgio Torrieri; Steve Steinke; Wojciech Broniowski; Wojciech Florkowski; Jean Letessier; Johann Rafelski
2004-07-22
SHARE is a collection of programs designed for the statistical analysis of particle production in relativistic heavy-ion collisions. With the physical input of intensive statistical parameters, it generates the ratios of particle abundances. The program includes cascade decays of all confirmed resonances from the Particle Data Tables. The complete treatment of these resonances has been known to be a crucial factor behind the success of the statistical approach. An optional feature implemented is a Breit--Wigner type distribution for strong resonances. An interface for fitting the parameters of the model to the experimental data is provided.
Johnstone, Daniel; Milward, Elizabeth A.; Berretta, Regina; Moscato, Pablo
2012-01-01
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
2014-12-19
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.
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
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
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
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
Wang, Jin
Modeling and Generating Daily Changes in Market Variables Using A Multivariate Mixture of Normal of the normal distribution for modeling of daily changes in market variables with fatter-than-normal tails is to transform (linearly) a multivariate normalwith an input covariance matrix into the desired multivariate
15.075 Applied Statistics, Spring 2003
Newton, Elizabeth
This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and ...
Discrimination of particle masses in multivariant space-time geometry
Yuri A. Rylov
2007-12-11
Multivariance of geometry means that at the point $P_{0}$ there exist many vectors $P_{0}P_{1}$, $\\P_{0}P_{2}$,... which are equivalent (equal) to the vector $\\Q_{0}Q_{1}$ at the point $Q_{0}$, but they are not equivalent between themselves. The discrimination capacity (zero-variance) of geometry appears, when at the point $P_{0}$ there are no vectors, which are equivalent to the vector $Q_{0}Q_{1}$ at the point $Q_{0}$. It is shown, that in some multivariant space-time geometries some particles of small mass may be discriminated (i.e. either they do not exist, or their evolution is impossible). The possibility of some particle discrimination may appear to be important for explanation of the discrete character of mass spectrum of elementary particles.
An algebraic interpretation of the multivariate $q$-Krawtchouk polynomials
Vincent X. Genest; Sarah Post; Luc Vinet
2015-08-31
The multivariate quantum $q$-Krawtchouk polynomials are shown to arise as matrix elements of "$q$-rotations" acting on the state vectors of many $q$-oscillators. The focus is put on the two-variable case. The algebraic interpretation is used to derive the main properties of the polynomials: orthogonality, duality, structure relations, difference equations and recurrence relations. The extension to an arbitrary number of variables is presented
Various forms of indexing HDMR for modelling multivariate classification problems
Aksu, Ça?r?; Tunga, M. Alper
2014-12-10
The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
Columbia University
Department of Statistics, Center for Theoretical Neuroscience and Grossman Center for the Statistics of Mind progress in Bayesian modeling, with applications to many areas of applied statistics and machine learning-Fejer kernels [Polson and Scott, 2011], and many others. The standard approach to sample from TMGs is the Gibbs
Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
Yuste, Rafael
Department of Statistics, Center for Theoretical Neuroscience and Grossman Center for the Statistics of Mind modeling, with applications to many areas of applied statistics and machine learning [Gelman et al., 2004 others. The standard approach to sample from TMGs is the Gibbs sampler [Geweke, 1991, Kotecha and Djuric
FISHERY STATISTICS UNITED STATES
FISHERY STATISTICS OF THE UNITED STATES 1972 STATISTICAL DIGEST NO. 66 Prepared by STATISTICS;ACKNOWLEDGMENTS The data in this edition of "Fishery Statistics of the United States" were collected in co- operation with the various States and tabulated by the staff of the Statistics and Market News Division
NREL: Awards and Honors - Real-Time Biomass Analysis
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
and saves trees from being prematurely harvested. The key to RTBA is the use of multivariate analysis to calibrate the near-infrared spectrometry to the specific parameters...
Donges, Jonathan F; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen
2015-01-01
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence qua...
Optimization Online - A data-driven, distribution-free, multivariate ...
Pavithra Harsha
2015-06-03
Jun 3, 2015 ... Furthermore, many other drivers besides price must be included in the demand response model for statistical accuracy, along with conditional ...
Braga-Neto, Ulisses
Intrinsically Multivariate Predictive Genes David C. Martins-Jr, Ulisses Braga-Neto, Ronaldo F multivariate predictive (IMP) genes for binary gene expression, and present a mathematical study of IMP;INTRINSICALLY MULTIVARIATE PREDICTIVE GENES 1 Index Terms Intrinsically Multivariate Prediction, Biological
GIS, SPATIAL STATISTICAL GRAPHICS, AND FOREST HEALTH.
Symanzik, Jürgen
1 GIS, SPATIAL STATISTICAL GRAPHICS, AND FOREST HEALTH. James J. Majure, Noel Cressie, Dianne Cook, and Jürgen Symanzik ABSTRACT This paper discusses the use of a geographic information systems (GIS), Arcview, into a geographic information system (GIS), Arcview 2.1 (ESRI 1995), and its use in the statistical analysis of spa
The University of Chicago Department of Statistics
- dimensional data. In this context, graphical models can act as a tool for regularization and have proven to be excellent tools for the analysis of high dimensional data. Graphi- cal models are statistical models where procedures for graphical models have recently received much attention in the statistics literature. The hyper
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2013-01-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Improving multivariate Horner schemes with Monte Carlo tree search
J. Kuipers; J. A. M. Vermaseren; A. Plaat; H. J. van den Herik
2012-07-30
Optimizing the cost of evaluating a polynomial is a classic problem in computer science. For polynomials in one variable, Horner's method provides a scheme for producing a computationally efficient form. For multivariate polynomials it is possible to generalize Horner's method, but this leaves freedom in the order of the variables. Traditionally, greedy schemes like most-occurring variable first are used. This simple textbook algorithm has given remarkably efficient results. Finding better algorithms has proved difficult. In trying to improve upon the greedy scheme we have implemented Monte Carlo tree search, a recent search method from the field of artificial intelligence. This results in better Horner schemes and reduces the cost of evaluating polynomials, sometimes by factors up to two.
Interpreting Accident Statistics
Ferreira, Joseph Jr.
Accident statistics have often been used to support the argument that an abnormally small proportion of drivers account for a large proportion of the accidents. This paper compares statistics developed from six-year data ...
Office of Energy Efficiency and Renewable Energy (EERE)
This page provides EERE Web statistics for all office and corporate websites that opted to use EERE's analytics account. Webtrends statistics for Fiscal Year 2009 (FY09) to FY11 are available for...
Office of Energy Efficiency and Renewable Energy (EERE)
EERE uses Google Analytics to capture statistics on its websites. These statistics help website managers measure and report on users, sessions, most visited pages, and more. The Web Template...
Babu, G. Jogesh
. Their work in astrostatistics includes multivariate methods for satellite data on Gamma-ray bursts in determining the size of the universe. Analyzed the Third Catalog of Gamma-Ray bursts data from BATSE on board the Compton Gamma Ray Observatory, using multivariate analysis. Babu, Feigelson along with a group
MATHMATICS & APPLIED STATISTICS
Frey, Jesse C.
MATHMATICS & APPLIED STATISTICS Graduate Studies in Build Your Future with Graduate Study in Mathematics or Applied Statistics Our graduate programs can help you advance your career in education will deepen your knowledge and prepare you for further study. The Master of Science in Applied Statistics
Statistics and Actuarial Science
Chauve, Cedric
SCIENCE SFU.CA/ SCIENCE Statistics and Actuarial Science #12;Further Information Student info, academic calendar, registration students.sfu.ca Science advising sfu.ca/science/undergrad/advising Statistics and Actuarial Science The Department of Statistics and Actuarial Science offers the degree
Physics 630 Statistical Physics
Kioussis, Nicholas
strongly the issue of problem solving and understanding of the main concepts in Statistical PhysicsPhysics 630 Statistical Physics Spring 2005 Logistics Lecture Room: 1100 (Science I, 1st floor (Supplement) Introduction to Modern Statistical Mechanics, by David Chandler, Oxford Objectives This course
Derrien, H.; Harvey, J.A.; Larson, N.M.; Leal, L.C.; Wright, R.Q.
2000-05-01
The average {sup 235}U neutron total cross sections were obtained in the energy range 2 keV to 330 keV from high-resolution transmission measurements of a 0.033 atom/b sample.1 The experimental data were corrected for the contribution of isotope impurities and for resonance self-shielding effects in the sample. The results are in very good agreement with the experimental data of Poenitz et al.4 in the energy range 40 keV to 330 keV and are the only available accurate experimental data in the energy range 2 keV to 40 keV. ENDF/B-VI evaluated data are 1.7% larger. The SAMMY/FITACS code 2 was used for a statistical model analysis of the total cross section, selected fission cross sections and data in the energy range 2 keV to 200 keV. SAMMY/FITACS is an extended version of SAMMY which allows consistent analysis of the experimental data in the resolved and unresolved resonance region. The Reich-Moore resonance parameters were obtained 3 from a SAMMY Bayesian fits of high resolution experimental neutron transmission and partial cross section data below 2.25 keV, and the corresponding average parameters and covariance data were used in the present work as input for the statistical model analysis of the high energy range of the experimental data. The result of the analysis shows that the average resonance parameters obtained from the analysis of the unresolved resonance region are consistent with those obtained in the resolved energy region. Another important result is that ENDF/B-VI capture cross section could be too small by more than 10% in the energy range 10 keV to 200 keV.
On-line reoptimization of filter designs for multivariate optical elements
Myrick, Michael Lenn
On-line reoptimization of filter designs for multivariate optical elements Frederick G. Haibach method for producing multivariate optical element MOE interference filters that are robust to errors severe adverse effects on the predictive ability of the MOE. Adaptive reoptimization of the filter design
Multivariate calibration with single-index signal regression Paul H.C. Eilers a
Marx, Brian D.
February 2009 Keywords: Multivariate calibration P-splines Projection pursuit regression In general, linearity is assumed to hold in multivariate calibration, but this may not be true. Penalized signal. The higher-dimensional space is never explicitly constructed: it is implied by the kernel function. We
Note on Design Criteria for Rainbow-Type Multivariates Jintai Ding1
International Association for Cryptologic Research (IACR)
Note on Design Criteria for Rainbow-Type Multivariates Jintai Ding1 , Bo-Yin Yang2 , Lei Hu3 , Jiun This was a short note that deals with the design of Rainbow or "stagewise unbalanced oil-and-vinegar" multivariate parameters in current schemes. These can be ameliorated according to an updated list of security design
Turning Tangent Empirical Mode Decomposition: a framework for mono-and multivariate signals
Paris-Sud XI, Université de
processing with a very important topic of research and development in various fields such as biomedical1 Turning Tangent Empirical Mode Decomposition: a framework for mono- and multivariate signals-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches
Skogestad, Sigurd
Achievable performance of multivariable systems with unstable zeros and poles K. HAVRE{{ and S) zeros and poles in multivariable feedback systems. We generalize previously known controller depend on the plant G. The bounds are tight for cases with only one RHP-zero or pole. For plants with RHP
GEOS36502/EVOL33002: Paleobiological Modeling and Analysis 2 (Multivariate Analysis) 1 Winter 2013
MDSij )2 d2 MDSij where ^d is the value of dMDS that would be required to yield a perfectly monotonic
Key China Energy Statistics 2011
Levine, Mark
2013-01-01
Source: National Bureau of Statistics (NBS), China EnergyNations Commodity Trade Statistics Database. New York:National Bureau of Statistics of the People's Republic of
Key China Energy Statistics 2012
Levine, Mark
2013-01-01
Nations Commodity Trade Statistics Database. New York:National Bureau of Statistics of the People's Republic ofYearbook. Beijing: China Statistics Press. 2. Transformation
Statistics and the Modern Student
Robert Gould
2011-01-01
Technology Innovations in Statistics Education, 3(1). Wild,the "wider view" of statistics, The American Statistician,a History of Teaching Statistics, Edinburgh: John Bibby (
Statistics and the Modern Student
Gould, Robert
2010-01-01
Technology Innovations in Statistics Education, 3(1). Wild,the "wider view" of statistics, The American Statistician,a History of Teaching Statistics, Edinburgh: John Bibby (
Statistics 221 Statistical Computing Methods Instructor: Mark Irwin
Irwin, Mark E.
Linear algebra, Statistics 111, and knowledge of a computer programming language. Statistics 220 (1988). Elements of Statistical Computing: Numerical Computation. CRC Press. Splus / R: Venables WNStatistics 221 Â Statistical Computing Methods Instructor: Mark Irwin Office: Science Center 235
J4.1 A MULTIVARIATE APPROACH TO MAPPING FOREST VEGETATION AND FUELS USING GIS DATABASES, SATELLITE
J4.1 A MULTIVARIATE APPROACH TO MAPPING FOREST VEGETATION AND FUELS USING GIS DATABASES, SATELLITE (Moeur and Stage 1995). The Gradient Nearest Neighbor (GNN) method, which combines a multivariate
1 Statistics Statistics plays an important role throughout society, providing
Vertes, Akos
1 Statistics STATISTICS Statistics plays an important role throughout society, providing data. They also explore how those skills can be applied to develop new initiatives. Statistics is one. UNDERGRADUATE Bachelor's program · Bachelor of Science with a major in statistics (http:// bulletin.gwu.edu/arts-sciences/statistics
F. Benatti; M. Fannes
1998-11-26
We use multi-time correlation functions of quantum systems to construct random variables with statistical properties that reflect the degree of complexity of the underlying quantum dynamics.
Kevin Cahill
2006-12-24
The way a field transforms under rotations determines its statistics--as is easy to see for scalar, Dirac, and vector fields.
Edinburgh Research Explorer Statistical Constraints
Millar, Andrew J.
Edinburgh Research Explorer Statistical Constraints Citation for published version: Rossi, R that links statistics and constraint programming. We dis- cuss two novel statistical constraints and some, Prestwich, S & Tarim, SA 2014, 'Statistical Constraints' Paper presented at 21st biennial European
Statistical Computing with R Eric Slud, Math. Dept., UMCP
Maryland at College Park, University of
Statistical Computing with R Eric Slud, Math. Dept., UMCP October 21, 2009 Overview of Course as indicated on the Course Syllabus. These fall roughly into three main headings: (A). R (& SAS) language and implementation of statistical algorithms, primarily in R; and (C). Data analysis and statistical applications
Statistical Computing with R Eric Slud, Math. Dept., UMCP
Maryland at College Park, University of
Statistical Computing with R Eric Slud, Math. Dept., UMCP August 30, 2009 Overview of Course as indicated on the Course Syllabus. These fall roughly into three main headings: (A). R (& SAS) language and implementation of statistical algorithms, primarily in R; and (C). Data analysis and statistical applications
Reimchen, Thomas E.
Multivariate differentiation of parapatric and allopatric populations of threespine stickleback REIMCHEN,T. E., E. M. STINSON,and J. S. NELSON.1985. Multivariate differentiation of parapatric (trachurus)from a nearby locality in marine waters. While multivariate means were significantly different
Walsh, Bruce
29 Measuring Multivariate Selection We have found that there are fundamental differences between all the measures of phenotypic selection discussed here are multivariate extensions of measures is as follows: We first introduce the multivariate versions of differentials and gradients and their properties
A multivariate view of the evolution of sexual dimorphism M. J. WYMAN, J. R. STINCHCOMBE & L. ROWE
Sokolowski, Marla
A multivariate view of the evolution of sexual dimorphism M. J. WYMAN, J. R. STINCHCOMBE & L. ROWE. Widespread dimorphism, despite a shared genome, may be more readily explained by considering the multivariate. By contrast, the multivariate formulation has greater generality and more flexibility. Although the number
Gunst, R. F.
2013-05-01
Phase 3 of the EPAct/V2/E-89 Program investigated the effects of 27 program fuels and 15 program vehicles on exhaust emissions and fuel economy. All vehicles were tested over the California Unified Driving Cycle (LA-92) at 75 degrees F. The program fuels differed on T50, T90, ethanol, Reid vapor pressure, and aromatics. The vehicles tested were new, low-mileage 2008 model year Tier 2 vehicles. A total of 956 test runs were made. Comprehensive statistical modeling and analyses were conducted on methane, carbon dioxide, carbon monoxide, fuel economy, non-methane hydrocarbons, non-methane organic gases, oxides of nitrogen, particulate matter, and total hydrocarbons. In general, model fits determined that emissions and fuel economy were complicated by functions of the five fuel parameters. An extensive evaluation of alternative model fits produced a number of competing model fits. Many of these alternative fits produce similar estimates of mean emissions for the 27 program fuels but should be carefully evaluated for use with emerging fuels with combinations of fuel parameters not included here. The program includes detailed databases on each of the 27 program fuels on each of the 15 vehicles and on each of the vehicles on each of the program fuels.
Weakly sufficient quantum statistics
Katarzyna Lubnauer; Andrzej ?uczak; Hanna Pods?dkowska
2009-11-23
Some aspects of weak sufficiency of quantum statistics are investigated. In particular, we give necessary and sufficient conditions for the existence of a weakly sufficient statistic for a given family of vector states, investigate the problem of its minimality, and find the relation between weak sufficiency and other notions of sufficiency employed so far.
Statistics Statistique Canada Canada
Canada Développement social Canada Culture,Tourism and the Centre for Education Statistics Doctoral, Tourism and the Centre for Education Statistics Division Main Building, Room 2001, Ottawa, K1A 0T6 to access the product This product, Catalogue no. 81-595-M, is available for free in electronic format
Topology for statistical modeling of petascale data.
Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)
2011-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.
Spin - or, actually: Spin and Quantum Statistics
Juerg Froehlich
2008-02-29
The history of the discovery of electron spin and the Pauli principle and the mathematics of spin and quantum statistics are reviewed. Pauli's theory of the spinning electron and some of its many applications in mathematics and physics are considered in more detail. The role of the fact that the tree-level gyromagnetic factor of the electron has the value g = 2 in an analysis of stability (and instability) of matter in arbitrary external magnetic fields is highlighted. Radiative corrections and precision measurements of g are reviewed. The general connection between spin and statistics, the CPT theorem and the theory of braid statistics are described.