Varying-Coefficient Functional Linear Regression Models
Cardot, Hervé
Varying-Coefficient Functional Linear Regression Models Herv´e Cardot1 and Pascal Sarda2 1, the ability of such non linear functional approaches to produce competitive estimations. Short title : Varying monograph. We propose here another generalization of the functional linear regression model in which
Non-linear regression models for Approximate Bayesian Computation
Robert, Christian P.
Non-linear regression models for Approximate Bayesian Computation (ABC) Michael Blum Olivier ABC #12;Blum and OF (2009) suggest the use of non-linear conditional heteroscedastic regression models) Linear regression-based ABC can sometimes be improved #12;abc of ABC Using stochastic simulations
Bootstrap Tests for Overidentification in Linear Regression Models
Spino, Claude
Bootstrap Tests for Overidentification in Linear Regression Models Russell Davidson Department it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap are not too weak. We also study the power properties of the bootstrap tests. JEL codes: C10, C12, C15, C30
Linear Regression and Support Vector Regression
Shi, Qinfeng "Javen"
Linear Regression and Support Vector Regression Paul Paisitkriangkrai paulp@cs.adelaide.edu.au The University of Adelaide 18 August 2014 #12;Outlines · Regression overview · Linear regression · Support vector regression · Machine learning tools available #12;Regression Overview CLUSTERING CLASSIFICATION REGRESSION
Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial
Shen, Haipeng
Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach of functional coefficient regression models for non-linear time series. Consistency and rate of convergence regression model extends several familiar non-linear time series models such as the exponential
West, Mike
of covariates to use in regression or generalized linear models is a ubiquitous problem. The Bayesian paradigm regression and binary re- gression with non-orthogonal design matrices in conjunction with independent "spike and kernel regression (Clyde and George 2004). The generalization of the Gaussian linear model to other
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In gen- eral, these problems entail non
Open source software maturity model based on linear regression and Bayesian analysis
Zhang, Dongmin
2009-05-15T23:59:59.000Z
based on Bayesian statistics. More importantly, an updating rule is established through Bayesian analysis to improve the joint distribution, and thus the objectivity of the coefficients in the linear multiple-regression model, according to new incoming...
ENGI 3423 Simple Linear Regression Page 12-01 Simple Linear Regression
George, Glyn
for dealing with non-linear regression are available in the course text, but are beyond the scopeENGI 3423 Simple Linear Regression Page 12-01 Simple Linear Regression Sometimes an experiment predict the value of Y for that value of x . The simple linear regression model is that the predicted
Cambridge, University of
30 8. Neural Networks Over the years, linear regression models have attempted to characterise the 0 interact. A more powerful alternative is the use of neural networks [40,42], a non-linear modelling prediction uncertainties. #12;31 In linear regression, the sum of each input xi multiplied with a weight wi
Simple Linear Regression Basic Ideas
Simple Linear Regression Basic Ideas Some Examples Least Squares Statistical View of Least Squares of Least Squares #12;Simple Linear Regression Basic Ideas Some Examples Least Squares Basic Ideas Suppose #12;Simple Linear Regression Basic Ideas Some Examples Least Squares Basic Ideas Suppose we have two
Computational Reality XIII Non-linear regression
Berlin,Technische Universität
Computational Reality XIII Non-linear regression Inverse analysis II B. Emek Abali @ LKM - TU Berlin Abstract Linear regression to fit and determine parameters, shown in the last tutorial, is quite useful and widely implemented, however, there are material models where parameters are coupled non-linearly
Linear regression issues in astronomy
Babu, G. Jogesh
Linear regression issues in astronomy Eric Feigelson Summer School in astrostatistics References regression Seeking the intrinsic relationship between two properties without specifying `dependent' and `independent' variables OLS(Y|X) OLS(X|Y) (inverse regr) Four symmetrical regression lines #12;Analytical
Math 261A -Spring 2012 M. Bremer Multiple Linear Regression
Keinan, Alon
called non-linear regression models or polynomial regression models, as the regression curveMath 261A - Spring 2012 M. Bremer Multiple Linear Regression So far, we have seen the concept of simple linear regression where a single predictor variable X was used to model the response variable Y
Building an MLR Model Building a multiple linear regression (MLR) model from data is one of the
Olive, David
Chapter 3 Building an MLR Model Building a multiple linear regression (MLR) model from data is one- imation to the data can be difficult. Model building is an iterative process. Given the problem and data, spend about 1/8 of the budget to collect data and build an initial MLR model. Spend another 1
Nonlinear models Nonlinear Regression
Penny, Will
Nonlinear models Will Penny Nonlinear Regression Nonlinear Regression Priors Energies Posterior, UCL, March 2013 #12;Nonlinear models Will Penny Nonlinear Regression Nonlinear Regression Priors Example Sampling Metropolis-Hasting Proposal density References Nonlinear Regression We consider
Kernel Density Based Linear Regression Estimate and Zhibiao Zhao
Zhao, Zhibiao
Kernel Density Based Linear Regression Estimate Weixin Yao and Zhibiao Zhao Abstract For linear regression models with non-normally distributed errors, the least squares estimate (LSE) will lose some words: EM algorithm, Kernel density estimate, Least squares estimate, Linear regression, Maximum
Modeling Multiple Drugs on Lung Cancer and Normal Cells using Regression
Chen, Michelle
2013-01-01T23:59:59.000Z
GLM: Binomial Regression Model . . . . . . . . . .Linear Regression: Least SquaresCourse in Statistics Regression Analysis. Pearson Education,
Batch-mode Supervised Learning Linear regression
Wehenkel, Louis
Batch-mode Supervised Learning Linear regression Applied inductive learning - Lecture 3 Louis (& Pierre Geurts)AIA... (1/19) #12;Batch-mode Supervised Learning Linear regression Batch-mode Supervised Learning Linear regression Least mean square error solution Regularization and algorithmics Residual
Automating approximate Bayesian computation by local linear regression
Thornton, Kevin R
2009-01-01T23:59:59.000Z
computation by local linear regression Kevin R Thorntonof ABC based on using a linear regression to approximate theimplements the local linear-regression approach to ABC. The
Masuda, H.; Claridge, D. E.
2012-01-01T23:59:59.000Z
Inclusion?of?Building?Envelope?Thermal?Lag? Effects?in?Linear?Regression?Models?of?Daily? Basis?Building?Energy?Use?Data The?12th International?Conference?for?Enhanced?Building?Operations October?22nd?26th,?2012 Manchester,?UK Hiroko...?enhanced?building?operations. October?18?20,?2011,? Brooklyn,?NY. Rabl,?A.?and?Rialhe,?A.?(1992).?Energy?Signature?Models?for?Commercial?Buildings:?Test?with?Measured?Data?and?Interpretation. Energy?and?Buildings,?19,?143?154. Shao,?X.?and?Claridge,?D.E.?(2006).?Use?of?first?law...
Masuda, H.; Claridge, D. E.
2012-01-01T23:59:59.000Z
Inclusion?of?Building?Envelope?Thermal?Lag? Effects?in?Linear?Regression?Models?of?Daily? Basis?Building?Energy?Use?Data The?12th International?Conference?for?Enhanced?Building?Operations October?22nd?26th,?2012 Manchester,?UK Hiroko...?enhanced?building?operations. October?18?20,?2011,? Brooklyn,?NY. Rabl,?A.?and?Rialhe,?A.?(1992).?Energy?Signature?Models?for?Commercial?Buildings:?Test?with?Measured?Data?and?Interpretation. Energy?and?Buildings,?19,?143?154. Shao,?X.?and?Claridge,?D.E.?(2006).?Use?of?first?law?energy?balance?as?a?screening?tool?for?building?energy...
1D Regression ... estimates of the linear regression coefficients are relevant to the linear
Olive, David
Chapter 12 1D Regression ... estimates of the linear regression coefficients are relevant and look for a functional form for g(·). Brillinger (1983, p. 98) Regression is the study. The scalar Y is a random variable and x is a random vector. A special case of regression is multiple linear
1D Regression ... estimates of the linear regression coefficients are relevant to the linear
Olive, David
Chapter 15 1D Regression ... estimates of the linear regression coefficients are relevant and look for a functional form for g(·). Brillinger (1983, p. 98) Regression is the study. The scalar Y is a random variable and x is a random vector. A special case of regression is multiple linear
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01T23:59:59.000Z
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
Conventional regression models Unlabelled units
McCullagh, Peter
Conventional regression models Unlabelled units Consequences Sampling bias in logistic models Peter effects #12;Conventional regression models Unlabelled units Consequences Outline 1 Conventional regression models Gaussian models Binary regression model Properties of conventional models 2 Unlabelled units Point
de Souza, R S; Buelens, B; Riggs, J D; Cameron, E; Ishida, E E O; Chies-Santos, A L; Killedar, M
2015-01-01T23:59:59.000Z
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for e...
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01T23:59:59.000Z
in Multi-response Linear Regression Problem Set-up and theList of Figures The linear regression model Y ? X?, with p nin high-dimensional multivariate regression. ” The Annals of
Review: Logistic regression, Gaussian nave Bayes, linear regression, and their connections
Mitchell, Tom
Review: Logistic regression, Gaussian naïve Bayes, linear regression, and their connections Yi, and feature selection #12;Outline Logistic regression Decision surface (boundary) of classifiers Generative vs. discriminative classifiers Linear regression Bias-variance decomposition and tradeoff
In-situ prediction on sensor networks using distributed multiple linear regression models
Basha, Elizabeth (Elizabeth Ann)
2010-01-01T23:59:59.000Z
Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where ...
The Equivalence between Row and Column Linear Regression
Tresp, Volker
The Equivalence between Row and Column Linear Regression: A Surprising Feature of Linear Regression and Communications 81730 M¨unchen, Germany Abstract The rows of the design matrix in linear regression are the inputs of the training data set. Normal regression is row regression: the goal is to predict a training target from
Parameter-insensitive kernel in extreme learning for non-linear support vector regression
Verleysen, Michel
Parameter-insensitive kernel in extreme learning for non-linear support vector regression Beno for regression which uses the e-sensitive loss and produces sparse models. However, non-linear SVRs are difficult.g. [24]). Used in conjunction with kernels, SVRs are powerful non-linear models for regression which
Obradovic, Zoran
of accuracy for both linear and non-linear regression models. The obtained experimental results suggest impact on accuracy of an auto-regression model. For non-linear phenomena, learning algorithms that model grid. The proposed method combines linear or non-linear non-spatial and non- temporal regression models
Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method
Cichocki, Andrzej
1 Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method Qibin Regression (PLSR) - a multivariate method which, in contrast to Multiple Linear Regression (MLR. A standard way to optimize the model parameters is the Non- linear Iterative Partial Least Squares (NIPALS
The SROV program for data analysis and regression model identification
Brauner, Neima
) regression models comprised of linear combination of independent variables and their non-linear functions. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Stepwise regression; Colinearity; Non have been intro- duced for selection of the optimal model in linear regression (for detailed reviews
Model selection and estimation of a component in additive regression
Paris-Sud XI, Université de
on s and is based on non-asymptotic model selection methods. Given some linear spaces collection {Sm, m M}, we proposed and, among them, a widely used is the linear regression Z = µ + k i=1 iX(i) + (2) where µ;drawback of linear regression is its lack of flexibility for modeling more complex dependencies between Z
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference regression models Auto-generated units Consequences of auto-generation Inference and prediction Outline 1 Conventional regression models Gaussian models Binary regression model Properties of regression models Problems
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference Royal Statistical Society Feb 6, 2008 Peter McCullagh Auto-generated units #12;Conventional regression Conventional regression models Gaussian models Binary regression model Properties of regression models Problems
Topics on Regularization of Parameters in Multivariate Linear Regression
Chen, Lianfu
2012-02-14T23:59:59.000Z
My dissertation mainly focuses on the regularization of parameters in the multivariate linear regression under different assumptions on the distribution of the errors. It consists of two topics where we develop iterative procedures to construct...
Testing regression models with fewer regressors
Judea Pearl; P. Meshkat
2011-01-01T23:59:59.000Z
Testing Regression Models W i t h Fewer Regressors JudeaINTRODUCTION Let M be a recursive regression model, that is,a set of regression equations for ordered variables Yi, ,
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference regression models Auto-generated units Consequences of auto-generation Inference and prediction Outline 1 Conventional regression models Gaussian models Binary regression model Attenuation of treatment effect Problems
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Arguments pro-generated units #12;Conventional regression models Auto-generated units Consequences of auto-generation Arguments pro and con Outline 1 Conventional regression models Gaussian models Binary regression model
arXiv:1103.0628v1[astro-ph.IM]3Mar2011 Bivariate least squares linear regression
Masci, Frank
arXiv:1103.0628v1[astro-ph.IM]3Mar2011 Bivariate least squares linear regression: towards a unified squares linear regression, the classical ap- proach pursued for functional models in earlier attempts are regression lines in the general case of correlated errors in X and in Y for heteroscedastic data
Regression models Consequences of auto-generation
McCullagh, Peter
Regression models Consequences of auto-generation All creatures great and small Inference University of Chicago Statslab Cambridge Nov 14 2008 Peter McCullagh Auto-generated units #12;Regression Regression models Gaussian models Binary regression model Attenuation of treatment effect Problems
Statistical prediction of aircraft trajectory: regression methods vs point-mass model
Paris-Sud XI, Université de
the altitude of climbing aircraft. In addition to the standard linear regression model, two common non-linear, BADA, linear regression, neural networks, Loess. INTRODUCTION Predicting aircraft trajectoriesStatistical prediction of aircraft trajectory: regression methods vs point-mass model M. Ghasemi
EXTENSIONS OF GENERALIZED LINEAR MODELING APPROACH TO STOCHASTIC WEATHER GENERATORS
Katz, Richard
weather) -- Software R open source statistical programming language: Function glm "Family;(2) Generalized Linear Models Statistical Framework -- Multiple Regression Analysis (Linear model or LM) Response
Non-Linear Continuum Regression Using Genetic Programming Ben.McKay@ncl.ac.uk
Fernandez, Thomas
Non-Linear Continuum Regression Using Genetic Programming Ben McKay Ben.McKay@ncl.ac.uk Mark Willis In this contribution, genetic programming is combined with continuum regression to produce two novel non-linear-based' strategy. Having discussed continuum regression, the modifications required to extend the algorithm for non-linear
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
Robust linear regression with broad distributions of errors
Postnikov, Eugene B
2015-01-01T23:59:59.000Z
We consider the problem of linear fitting of noisy data in the case of broad (say $\\alpha$-stable) distributions of random impacts ("noise"), which can lack even the first moment. This situation, common in statistical physics of small systems, in Earth sciences, in network science or in econophysics, does not allow for application of conventional Gaussian maximum-likelihood estimators resulting in usual least-squares fits. Such fits lead to large deviations of fitted parameters from their true values due to the presence of outliers. The approaches discussed here aim onto the minimization of the width of the distribution of residua. The corresponding width of the distribution can either be defined via the interquantile distance of the corresponding distributions or via the scale parameter in its characteristic function. The methods provide the robust regression even in the case of short samples with large outliers, and are equivalent to the normal least squares fit for the Gaussian noises. Our discussion is il...
Regression Model for Daily Maximum Stream Temperature David W. Neumann1
Balaji, Rajagopalan
Regression Model for Daily Maximum Stream Temperature David W. Neumann1 ; Balaji Rajagopalan2 for the summer period. The model is created using a stepwise linear regression procedure to select significant-9372 2003 129:7 667 CE Database subject headings: Decision support systems; Regression models; California
Boyer, Edmond
a linear regression model. A generalization is the additive logistic model, which replaces each linear term, removes irrelevant variables, and identifies non linear trends. The estimates are computed via the usualParsimonious additive logistic models Logistic regression is a standard tool in statistics
Structure based chemical shift prediction using Random Forests non-linear regression
Langmead, Christopher James
Structure based chemical shift prediction using Random Forests non-linear regression K. Arun-ordinates will permit close study of this relationship. This paper presents a novel non-linear regression based ap, regression, Random Forests #12;Abstract Protein nuclear magnetic resonance (NMR) chemical shifts are among
Consistency of the posterior distribution and MLE for piecewise linear regression
Paris-Sud XI, Université de
Consistency of the posterior distribution and MLE for piecewise linear regression Tristan Launay1 and that of the Bayes estimator for a two-phase piecewise linear regression mdoel where the break-point is unknown and be the unknown regression coefficient of the non-zero phase. The observations X1:n = (X1, . . . , Xn) depend
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01T23:59:59.000Z
in high-dimensional multivariate regression. ” The Annals ofSupport Union Recovery in Multivariate Multi-Response LinearSupport Union Recovery in Multivariate Multi-Response Linear
Logistic Regression and Bayesian Model Selection in Estimation of Probability of Success
Shemyakin, Arkady
1 1 Logistic Regression and Bayesian Model Selection in Estimation of Probability of Success Arkady ABSTRACT Logistic regression and linear discriminant analysis are used to estimate probability of success X is analyzed as an explanatory variable. A comparison is made between logistic regression technique
Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
Kazhdan, Michael
Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression A. C. Öztireli, G IMLS in terms of Local Kernel Regression (LKR) · Borrowing ideas from robust statistics · Advantages to Implement · Competitive performance #12;Local Kernel Regression · Taylor expansion around the evaluation
STATISTICAL MODEL OF SYSTEMATIC ERRORS: LINEAR ERROR MODEL
Rudnyi, Evgenii B.
to apply. The algorithm to maximize a likelihood function in the case of a non-linear physico - the same variances of errors 3.1. One-way classification 3.2. Linear regression 4. Real case (vaporizationSTATISTICAL MODEL OF SYSTEMATIC ERRORS: LINEAR ERROR MODEL E.B. Rudnyi Department of Chemistry
Olive, David
1D Regression David J. Olive Southern Illinois University August 27, 2004 Abstract Regression regression, Y is independent of x given a single linear combination +T x of the predictors. Special cases of 1D regression include multiple linear regression, logistic regression, generalized linear models
Tank, David
Linear Regression of Eye Velocity on Eye Position and Head Velocity Suggests a Common Oculomotor Aksay, David W. Tank, and H. S. Seung. Linear regression of eye velocity on eye position and head this conclusion was drawn by performing a linear regression of eye velocity on eye position and head velocity
Ridge Regression Estimation Approach to Measurement Error Model
Shalabh
Ridge Regression Estimation Approach to Measurement Error Model A.K.Md. Ehsanes Saleh Carleton of the regression parameters is ill conditioned. We consider the Hoerl and Kennard type (1970) ridge regression (RR) modifications of the five quasi- empirical Bayes estimators of the regression parameters of a measurement error
Visualizing 1D Regression David J. Olive
Olive, David
regression, binary regression and general- ized linear models. If a good estimate ^b of some non a single linear combination T x of the predictors. Special cases of 1D regression include multiple linear(y) = + T x + e. Generalized linear models (GLM's) are also a special case of 1D regression. Some notation
A note on modelling cross correlations: hyperbolic secant regression
Smyth, Gordon K.
A note on modelling cross correlations: hyperbolic secant regression Gordon K. Smyth Department of size one. This al- lows regression type modelling of the correlation without unnecessary loss ~ is given in Section 2 that has useful performance down to samples of size one. This allows regression type
Blei, David M.
a linear transformation of co- variates through a possibly non-linear link function to generate a response of generalized linear models (DP-GLMs), a Bayesian nonparametric regression model that combines the advantages of gen- eralized linear models with the flexibility of nonpara- metric regression. A DP-GLM produces
Shaon Sahoo; Soumya Kanti Ganguly
2015-02-01T23:59:59.000Z
Contrary to the actual nonlinear Glauber model (NLGM), the linear Glauber model (LGM) is exactly solvable, although the detailed balance condition is not generally satisfied. This motivates us to address the issue of writing the transition rate ($w_j$) in a best possible linear form such that the mean squared error in satisfying the detailed balance condition is least. The advantage of this work is that, by studying the LGM analytically, we will be able to anticipate how the kinetic properties of an arbitrary Ising system depend on the temperature and the coupling constants. The analytical expressions for the optimal values of the parameters involved in the linear $w_j$ are obtained using a simple Moore-Penrose pseudoinverse matrix. This approach is quite general, in principle applicable to any system and can reproduce the exact results for one dimensional Ising system. In the continuum limit, we get a linear time-dependent Ginzburg-Landau (TDGL) equation from the Glauber's microscopic model of non-conservative dynamics. We analyze the critical and dynamic properties of the model, and show that most of the important results obtained in different studies can be reproduced by our new mathematical approach. We will also show in this paper that the effect of magnetic field can easily be studied within our approach; in particular, we show that the inverse of relaxation time changes quadratically with (weak) magnetic field and that the fluctuation-dissipation theorem is valid for our model.
Generating Fo contours from ToBI labels using linear regression
Black, Alan W; Hunt, Andrew
This paper describes a method for generating F0 contours from ToBI labelled utterances. The method uses linear regression to predict F0 target values for the start, mid-vowel and end of every syllable, using features representing the ToBI labels...
Regression Analysis with an Unknown Link Function: the Adjoint Projection Pursuit Regression
Naihua Duan
2011-01-01T23:59:59.000Z
Misspecified Non- linear Regression Models," Journal of theAdjoint Projection Pursuit Regression also give examples inL is the estimated linear regression of x on bx. Note Remark
PROBABILISTIC AUTO-ASSOCIATIVE MODELS AND SEMI-LINEAR PCA
Paris-Sud XI, Université de
to this family of approaches, non-linear transformation of the original data set [7, 3] too. The auto-associative neural networks can also be view as a non-linear PCA model [2, 27, 4, 19]. In [13] we propose the auto that the projection function is linear and let the regression function be arbitrary. We call the resulting AAM
Modeling nonlinear relationships in ERP data using mixed-effects regression with R examples
Modeling nonlinear relationships in ERP data using mixed-effects regression with R examples ANTOINE of event-related potential (ERP) data, the assumption of linearity comes at a cost and may significantly be relaxed and how to model nonlinear relationships between ERP amplitudes and predictor variables within
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
Inference for Covariate Adjusted Regression via Varying Coefficient Models 1
Müller, Hans-Georg
¸ENT¨URK AND HANS-GEORG M¨ULLER University of California, Davis We consider covariate adjusted regression (CAR consider a variant of (1), where one observes contaminated versions of predictors and response. Contamination of the variables in the regression model occurs through a multiplicative factor that is determined
FSR Methods for Second-Order Regression Models Hugh B. Crews
Boos, Dennis
approach to forward selection by using different -to-enter values for first-order and second-order terms-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number-order terms. Method performance is compared through Monte Carlo simulation, and an illustration is provided
FSR Methods for Second-Order Regression Models Hugh B. Crews1
Boos, Dennis
-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number first-order and second-order terms. Method performance is compared through Monte Carlo simulation optimization, selecting interaction and quadratic terms is important. In such applications, second-order terms
Desmarais, Michel C.
Linear Models of Student Skills for Static Data Michel C. Desmarais, Rhouma Naceur, and Behzad be considered a logistic regression. Only a handful of recent studies have looked at linear models based, there are no reports of linear models applied to static knowledge states data. We introduce different linear models
DOI 10.1007/s10994-013-5423-y Least-squares independence regression for non-linear
Kaski, Samuel
Mach Learn DOI 10.1007/s10994-013-5423-y Least-squares independence regression for non-linear 2011 / Accepted: 9 November 2013 © The Author(s) 2013 Abstract The discovery of non-linear causal method. Keywords Causal inference · Non-linear · Non-Gaussian · Squared-loss mutual information · Least
Shetty, Rahul; Bigiel, Frank
2012-01-01T23:59:59.000Z
We develop a Bayesian linear regression method which rigorously treats measurement uncertainties, and accounts for hierarchical data structure for investigating the relationship between the star formation rate and gas surface density. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). Using synthetic datasets, we demonstrate that the Bayesian method accurately recovers the parameters of both the individuals and the population, especially when compared to commonly employed least squares methods, such as the bisector. We apply the Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). We find significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs between galaxies...
Jahandideh, Sepideh [Department of Hospital Management, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of)], E-mail: jahandideh@sums.ac.ir; Jahandideh, Samad [Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Asadabadi, Ebrahim Barzegari [Department of Biophysics, Faculty of Science, Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Askarian, Mehrdad [Department of Community Medicine, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Movahedi, Mohammad Mehdi [Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Hosseini, Somayyeh [Department of Biochemistry, Division of Genetics, Tabriz University of Medical Sciences, Tabriz (Iran, Islamic Republic of); Jahandideh, Mina [Department of Mathematics, Faculty of Science, Vali-E-Asr University, Rafsanjan (Iran, Islamic Republic of)
2009-11-15T23:59:59.000Z
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.
Paris-Sud XI, Université de
Non-asymptotic Adaptive Prediction in Functional Linear Models ´Elodie Brunel, Andr´e Mas, and Angelina Roche I3M, Universit´e Montpellier II Abstract Functional linear regression has recently attracted. Functional linear regression, functional principal components analysis, mean squared prediction error
Logistic regression Weakly informative priors
Gelman, Andrew
Logistic regression Weakly informative priors Conclusions Bayesian generalized linear models default p #12;Logistic regression Weakly informative priors Conclusions Classical logistic regression The problem of separation Bayesian solution Logistic regression -6 -4 -2 0 2 4 6 0.00.20.40.60.81.0 y = logit
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
Chen, Sheng
for costly model evaluation. Index Terms -- orthogonal forward regression, structure identification, cross struc- ture construction process as a cost function in order to op- timize the model generalization introduces a construction algorithm for sparse kernel modelling using the leave-one-out test score also known
Submitted to the Annals of Statistics FUNCTIONAL ADDITIVE REGRESSION
Radchenko, Peter
extends beyond the standard linear regression setting to fit general non-linear additive models. We extending the classical functional regression model. [25] proposed an index model to implement a non-linear extends the usual linear regression model involving a functional predictor, X(t), and a scalar response, Y
Arhonditsis, George B.
Multiple regression models: A methodology for evaluating trihalomethane concentrations in drinking of these substances on human health. A multiple regression model was developed to estimate THM concentrations Science Ltd. All rights reserved. Keywords: Multiple regression model; Trihalomethanes; Drinking water
A regression model with a hidden logistic process for feature extraction from time series
Chamroukhi, Faicel
operation. The switch operations signals can be seen as time series presenting non-linearities and various changes in regime. Basic linear regression can not be adopted for this type of sig- nals because a constant linear relationship is not adapted. As alternative to linear regression, some authors use
Censored regression modeling in agricultural economics
Khee-Guan Tan, Andrew
1991-01-01T23:59:59.000Z
error or omitted variable problem. According to Heckman, it is possible to correct for the above problem by first 10 estimating the omitted variable, X;. Using probit analysis, X; is consistently estimated as the inverse of Mill's ratio, f... is, in essence, an extension of Hausman's asymptotic specification test to the censored model. Hausman's approach required that an estimate of Ex@ ? = N-iE(X'y), say ExY, be compared to an estimator which A is consistent and inefficient relative...
Efficient inference in general semiparametric regression models
Maity, Arnab
2009-05-15T23:59:59.000Z
. Note that (2.17) means that the non-zero Y-data within an indi- vidual marginally have the same mean R T i ? 2 + ?(Z i ), variance ? 2 + ? 2 u2 and common covariance ? 2 u2 . II.4.2.3. Likelihood Function The collection of parameters is B, consisting... .............................. 4 II.1. Introduction ......................... 4 II.2. Semiparametric Models with a Single Component ..... 8 II.2.1. Main Results .................... 8 II.2.2. General Functions of the Response and Double- Robustness ..................... 11 II.3...
Baird, Matthew David
2012-01-01T23:59:59.000Z
additively non-separable linear regression model. First,the additively non-separable linear regression model matchesThe additively non-separable linear regression model nests
Climate Multi-model Regression Using Spatial Smoothing Karthik Subbian
Banerjee, Arindam
Climate Multi-model Regression Using Spatial Smoothing Karthik Subbian Arindam Banerjee Abstract There are several Global Climate Models (GCMs) reported by var- ious countries to the Intergovernmental Panel on Climate Change (IPCC). Due to the varied nature of the GCM assumptions, the fu- ture projections
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
Hüllermeier, Eyke
to as choquistic regression, is to replace the linear function of pre- dictor variables, which is commonly used, it becomes possible to capture non-linear dependencies and in- teractions among predictor variables while regression, including the following ones: · Since the model is essentially linear in the in- put attributes
Partially linear models with unit roots
Juhl, Ted P.; Xiao, Z. J.
2005-10-01T23:59:59.000Z
~y tH110021 * H11002 [y tH110021 * !~e t H11002 [e t H11001g~x t !H11002 [g~x t !! Zf t 2 H11001o p ~1!+ PARTIALLY LINEAR MODELS WITH UNIT ROOTS 897 The theorem holds because 1 N 2 ( tH110051 N ~y tH110021 * H11002 [y tH110021 * ! 2 Zf t 2 nE~f 2 !s v... in econometrics+ One type of these models is the following partially linear regression: y t H11005g ' z t H11001g~x t !H11001e t , tH110051,+++,N, (1.1) where g~{! is an unknown real function and g is the vector of unknown param- eters that we want to estimate...
Geodesic Regression on Riemannian Manifolds P. Thomas Fletcher
Boyer, Edmond
- ing multiple linear regression in Rn . Here we are interested in the relationship between a non that one could choose, and it provides a direct generalization of linear regression to the manifold setting regression model is linear regression, due to its simplicity, ease of interpretation, and ability to model
The Omega Counter, a Frequency Counter Based on the Linear Regression
Rubiola, E; Bourgeois, P -Y; Vernotte, F
2015-01-01T23:59:59.000Z
This article introduces the {\\Omega} counter, a frequency counter -- or a frequency-to-digital converter, in a different jargon -- based on the Linear Regression (LR) algorithm on time stamps. We discuss the noise of the electronics. We derive the statistical properties of the {\\Omega} counter on rigorous mathematical basis, including the weighted measure and the frequency response. We describe an implementation based on a SoC, under test in our laboratory, and we compare the {\\Omega} counter to the traditional {\\Pi} and {\\Lambda} counters. The LR exhibits optimum rejection of white phase noise, superior to that of the {\\Pi} and {\\Lambda} counters. White noise is the major practical problem of wideband digital electronics, both in the instrument internal circuits and in the fast processes which we may want to measure. The {\\Omega} counter finds a natural application in the measurement of the Parabolic Variance, described in the companion article arXiv:1506.00687 [physics.data-an].
PERSONALIZED ILLUMINANCE MODELING USING INVERSE MODELING AND PIECEWISE LINEAR REGRESSION
Agogino, Alice M.
and greater energy efficiency across multiple independent building systems. In order. Of this, lighting accounts for 11% of energy use in residential buildings and 25% of the energy use in commercial buildings. Increased energy
Morse-Smale Regression Samuel Gerber, University of Utah
approximated by a linear model. This approach yields regression models that are amenable to interpretation capabilities of non-parametric methods. A classical 1 #12;approach to partition-based regression are regression a piece- wise constant model, treed regression (Alexander and Grimshaw, 1996) proposed linear models
Efficiency transfer for regression models with responses missing at random
Mueller, Uschi
Efficiency transfer for regression models with responses missing at random Ursula U. M that characteristics of the con- ditional distribution of Y given X can be estimated efficiently using complete case analysis. One can simply omit incomplete cases and work with an appro- priate efficient estimator without
Iterative gradient descent approach to multiple regression with fuzzy data
Bargiela, Andrzej
to multiple regression and lay foundation for a further generalisation to multiple non-linear regression dictates adoption of a more general viewpoint, regression variables are given as non-numerical entities of the parameters of the regression model have been derived only for the case of a simple linear regression, i
Clement, Prabhakar
2001-01-01T23:59:59.000Z
of this present study was to introduce a simple, easily understood method for carrying out non-linear regression: Microsoft Excel; Non-linear regression; Least squares; Iteration; Goodness of fit; Curve fit wwwComputer Methods and Programs in Biomedicine 65 (2001) 191200 A step-by-step guide to non-linear
Efficient Estimation in a Regression Model with Missing Responses
Crawford, Scott
2012-10-19T23:59:59.000Z
This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected...
Slicing Regression: A Link-free Regression Method
Ker-Chau Li
2011-01-01T23:59:59.000Z
H. V. (1983). Reverse regression, fairness, and employmentleast squares linear regression. Statistica Sinica FISHER,A. S. (1981). Linear regression after selection. J.
Testing Lack-of-Fit of Generalized Linear Models via Laplace Approximation
Glab, Daniel Laurence
2012-07-16T23:59:59.000Z
In this study we develop a new method for testing the null hypothesis that the predictor function in a canonical link regression model has a prescribed linear form. The class of models, which we will refer to as canonical ...
Turlapaty, Anish C. [Mississippi State University (MSU); Younan, Nicolas H. [Mississippi State University (MSU); Anantharaj, Valentine G [ORNL
2012-01-01T23:59:59.000Z
Currently, the only viable option for a global precipitation product is the merger of several precipitation products from different modalities. In this article, we develop a linear merging methodology based on spatiotemporal regression. Four highresolution precipitation products (HRPPs), obtained through methods including the Climate Prediction Center's Morphing (CMORPH), Geostationary Operational Environmental Satellite-Based Auto-Estimator (GOES-AE), GOES-Based Hydro-Estimator (GOES-HE) and Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) algorithms, are used in this study. The merged data are evaluated against the Arkansas Red Basin River Forecast Center's (ABRFC's) ground-based rainfall product. The evaluation is performed using the Heidke skill score (HSS) for four seasons, from summer 2007 to spring 2008, and for two different rainfall detection thresholds. It is shown that the merged data outperform all the other products in seven out of eight cases. A key innovation of this machine learning method is that only 6% of the validation data are used for the initial training. The sensitivity of the algorithm to location, distribution of training data, selection of input data sets and seasons is also analysed and presented.
A Hybrid GP Approach for Numerically Robust Symbolic Regression
Fernandez, Thomas
expressions encoded in tree structures to perform symbolic regression. A non-linear optimization method very common technique is linear regression, in which the model is a linear combina- tion of given base are polynomials (polynomial regression) or trigonometric poly- nomials (e.g. Fourier series). For general linearly
High dimensional linear inverse modelling
Cooper, Fenwick C
2015-01-01T23:59:59.000Z
We introduce and demonstrate two linear inverse modelling methods for systems of stochastic ODE's with accuracy that is independent of the dimensionality (number of elements) of the state vector representing the system in question. Truncation of the state space is not required. Instead we rely on the principle that perturbations decay with distance or the fact that for many systems, the state of each data point is only determined at an instant by itself and its neighbours. We further show that all necessary calculations, as well as numerical integration of the resulting linear stochastic system, require computational time and memory proportional to the dimensionality of the state vector.
Gross, Louis J.
Linear Regression and Correlation Notes Suppose there is a data set of n data points (xi , yi is reasonable. Then the least-squares line (regression line) that best fits these data, ^y = ^m x + ^b has the regression coefficients ^m and ^b chosen so as to minimize the sum of the square errors n i=1 ( yi - ^yi )2
Exact and Approximate REML for Heteroscedastic Regression
Smyth, Gordon K.
Exact and Approximate REML for Heteroscedastic Regression Gordon K. Smyth Department of Mathematics, the above het- eroscedastic regression model is the most general model of the type considered by LN98 and SV to estimate the het- eroscedastic regression model by way of two coupled generalized linear models
Daume III, Hal
Robust RVM Regression Using Sparse Outlier Model Kaushik Mitra, Ashok Veeraraghavan* and Rama, vashok, rama}@umiacs.umd.edu Abstract Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many com- puter
Regression problems for magnitudes
Castellaro, Silvia; Mulargia, Francesco; Kagan, Yan Y
2006-01-01T23:59:59.000Z
through general orthogonal regression with ? = 0.25 (? m2 =use and misuse of orthogonal regression in linear errors-in-A procedure for linear regression capable of less biased
Stevens, F. J.; Bobrovnik, S. A.; Biosciences Division; Palladin Inst. Biochemistry
2007-12-01T23:59:59.000Z
Physiological responses of the adaptive immune system are polyclonal in nature whether induced by a naturally occurring infection, by vaccination to prevent infection or, in the case of animals, by challenge with antigen to generate reagents of research or commercial significance. The composition of the polyclonal responses is distinct to each individual or animal and changes over time. Differences exist in the affinities of the constituents and their relative proportion of the responsive population. In addition, some of the antibodies bind to different sites on the antigen, whereas other pairs of antibodies are sterically restricted from concurrent interaction with the antigen. Even if generation of a monoclonal antibody is the ultimate goal of a project, the quality of the resulting reagent is ultimately related to the characteristics of the initial immune response. It is probably impossible to quantitatively parse the composition of a polyclonal response to antigen. However, molecular regression allows further parameterization of a polyclonal antiserum in the context of certain simplifying assumptions. The antiserum is described as consisting of two competing populations of high- and low-affinity and unknown relative proportions. This simple model allows the quantitative determination of representative affinities and proportions. These parameters may be of use in evaluating responses to vaccines, to evaluating continuity of antibody production whether in vaccine recipients or animals used for the production of antisera, or in optimizing selection of donors for the production of monoclonal antibodies.
Introduction Improved Model Alternative Statistical Model
Regression Linear "Linear" is for the parameter(s) e.g. yi = 0 +1xi +i Non-linear "Non-linear Square Regression Linear "Linear" is for the parameter(s) e.g. yi = 0 +1xi +i #12;Introduction Improved Model Recall of Ordinary Least-Square Regression Least Square Regression Linear "Linear
Testing regression models with residuals as data by Xia Hua.
Hua, Xia, Ph. D. Massachusetts Institute of Technology
2010-01-01T23:59:59.000Z
Abstract In polynomial regression ... . In this thesis, I developed a residual based test, the turning point test for residuals, which tests the hypothesis that the kth order polynomial regression holds with ... while the ...
Scranton, Katherine
2012-01-01T23:59:59.000Z
cois, 2008. Non-linear regression models for approximateparameters. They use non-linear regression of parameters on
Blei, David M.
2011-01-01T23:59:59.000Z
characterizes the deviation of the response from its conditional mean. The simplest example is linear regression. Generalized linear models (GLMs) extend linear regression to many types of response variables (Mc a linear function; a non-linear function may be applied to the output of the linear function, but only one
Modeling Animal-Vehicle Collisions Using Diagonal Inflated Bivariate Poisson Regression
Washington at Seattle, University of
1 Modeling Animal-Vehicle Collisions Using Diagonal Inflated Bivariate Poisson Regression of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets
Regression Models with Interval Censoring Jian Huang and Jon A. Wellner 1
Wellner, Jon A.
Regression Models with Interval Censoring Jian Huang and Jon A. Wellner 1 University of Washington October 6, 1993 Abstract In this paper we discuss estimation in semiparametric regression models with interval censoring, with emphasis on estimation of the regression parameter . The first section surveys
A regression model with a hidden logistic process for signal parametrization
Chamroukhi, Faicel
A regression model with a hidden logistic process for signal parametrization F. Chamroukhi, A. Samé (INRETS/UTC-France) ESANN 2009 April 24 2009 1 / 21 #12;Outline 1 Context 2 The piecewise regression approach 3 The proposed regression approach 4 Parameter estimation 5 Experiments Faicel Chamroukhi (INRETS
Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression
Thomas, David D.
Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression Etienne the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real
Comparison of co-expression measures: mutual information, correlation, and model based indices
Song, Lin; Langfelder, Peter; Horvath, Steve
2012-01-01T23:59:59.000Z
that non- linear association measures, especially regressioncontrast, regression models capture non-linear gene pairwiseand spline regression models to measure non-linear
West, Mike
classification, validation, prognosis Binary regression models · Linear regression model based on regression Standard statistical models transform from real-value to (0, 1) using a specified non-linear functionStatistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification
Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States)] [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States)] [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
2014-01-15T23:59:59.000Z
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.
Groping Toward Linear Regression Analysis: Newton's Analysis of Hipparchus' Equinox Observations
Ari Belenkiy; Eduardo Vila Echague
2008-10-27T23:59:59.000Z
In 1700, Newton, in designing a new universal calendar contained in the manuscripts known as Yahuda MS 24 from Jewish National and University Library at Jerusalem and analyzed in our recent article in Notes & Records Royal Society (59 (3), Sept 2005, pp. 223-54), attempted to compute the length of the tropical year using the ancient equinox observations reported by a famous Greek astronomer Hipparchus of Rhodes, ten in number. Though Newton had a very thin sample of data, he obtained a tropical year only a few seconds longer than the correct length. The reason lies in Newton's application of a technique similar to modern regression analysis. Actually he wrote down the first of the two so-called "normal equations" known from the Ordinary Least Squares method. Newton also had a vague understanding of qualitative variables. This paper concludes by discussing open historico-astronomical problems related to the inclination of the Earth's axis of rotation. In particular, ignorance about the long-range variation in inclination and nutation of the axis is likely responsible for the wide variety in the lengths of the tropical year assigned by different 17th century astronomers - the problem that led Newton to Hipparchus and to an "embryonic" regression analysis.
Johnston, Walter Edward
1965-01-01T23:59:59.000Z
*) )() yx V = y* ? ie x* yx 2 n 2 2 n 21 Z (yp-y*) -g ?Z (x -x") J/n, yx rl l n x* Q x(f ~ y fyiq) * n& ', n)(j Thus the estimates of p, tT, and p are obtained by solving (B. 4) 2 y y These estimates are (s. s) m y))) +P (x x*) Y yx 2 rl q /2 *2... factors, which are either fixed or follow a multinormal distribution, employed in the n repetitions of the experiment, B = a (p x 1) matrix of unknown partial regression coeffictents to be estimated. The approach to the problem of missing data...
Reduced-rank Vector Generalized Linear Models Thomas W. Yee,
Hastie, Trevor
. Keywords: Canonical correspondence analysis; Linear discriminant analysis; Neural networks; Non- parametric the reduced-rank regression idea has been applied to non-Gaussian errors is the MLM. This was proposed such as neural networks, projection pursuit regression, linear discriminant analysis, canonical correspondence
Multi-Anticipative Piecewise-Linear Car-Following Model
Nadir Farhi; Habib Haj-Salem; Jean-Patrick Lebacque
2013-02-01T23:59:59.000Z
We propose in this article an extension of the piecewise linear car-following model to multi-anticipative driving. As in the one-car-anticipative model, the stability and the stationary regimes are characterized thanks to a variational formulation of the car-dynamics. We study the homogeneous driving case. We show that in term of the stationary regime, the multi-anticipative model guarantees the same macroscopic behavior as for the one-car-anticipative one. Nevertheless, in the transient traffic, the variance in car-velocities and accelerations is mitigated by the multi-anticipative driving, and the car-trajectories are smoothed. A parameter identification of the model is made basing on NGSIM data and using a piecewise linear regression approach.
Groping Toward Linear Regression Analysis: Newton's Analysis of Hipparchus' Equinox Observations
Belenkiy, Ari
2008-01-01T23:59:59.000Z
In 1700, Newton, in designing a new universal calendar contained in the manuscripts known as Yahuda MS 24 from Jewish National and University Library at Jerusalem and analyzed in our recent article in Notes & Records Royal Society (59 (3), Sept 2005, pp. 223-54), attempted to compute the length of the tropical year using the ancient equinox observations reported by a famous Greek astronomer Hipparchus of Rhodes, ten in number. Though Newton had a very thin sample of data, he obtained a tropical year only a few seconds longer than the correct length. The reason lies in Newton's application of a technique similar to modern regression analysis. Actually he wrote down the first of the two so-called "normal equations" known from the Ordinary Least Squares method. Newton also had a vague understanding of qualitative variables. This paper concludes by discussing open historico-astronomical problems related to the inclination of the Earth's axis of rotation. In particular, ignorance about the long-range variation...
Non-parametric regression and neural-network inll drilling recovery models for carbonate reservoirs
Valkó, Peter
Non-parametric regression and neural-network in®ll drilling recovery models for carbonate This work introduces non-parametric regression and neural network models for forecasting the in®ll drilling variables. This situation mandates proper selection of independent variables for the in®ll drilling recovery
Photon emission within the linear sigma model
F. Wunderlich; B. Kampfer
2014-12-22T23:59:59.000Z
Soft-photon emission rates are calculated within the linear sigma model. The investigation is aimed at answering the question to which extent the emissivities map out the phase structure of this particular effective model of strongly interacting matter.
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
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
Kockelman, Kara M.
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity By Jianming Ma, and lead to potential biases in sample databases. This paper offers a multivariate Poisson specification severity, Gibbs sampler, Markov chain Monte Carlo (MCMC) simulation, multivariate Poisson regression #12
Regional regression models of watershed suspended-sediment discharge for the eastern United States
Vogel, Richard M.
: Sediment transport Regression Water quality Ungaged GAGES SPARROW s u m m a r y Estimates of mean annual Streamflow (GAGES) database. The resulting regional regression models summarized for major US water resources contaminants including pesticides, met- als, and polycyclic aromatic hydrocarbons (PAHs) readily sorb
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila
2009-06-28T23:59:59.000Z
This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.
Sharon Falcone Miller; Bruce G. Miller [Pennsylvania State University, University Park, PA (United States). Energy Institute
2007-12-15T23:59:59.000Z
This paper compares the emissions factors for a suite of liquid biofuels (three animal fats, waste restaurant grease, pressed soybean oil, and a biodiesel produced from soybean oil) and four fossil fuels (i.e., natural gas, No. 2 fuel oil, No. 6 fuel oil, and pulverized coal) in Penn State's commercial water-tube boiler to assess their viability as fuels for green heat applications. The data were broken into two subsets, i.e., fossil fuels and biofuels. The regression model for the liquid biofuels (as a subset) did not perform well for all of the gases. In addition, the coefficient in the models showed the EPA method underestimating CO and NOx emissions. No relation could be studied for SO{sub 2} for the liquid biofuels as they contain no sulfur; however, the model showed a good relationship between the two methods for SO{sub 2} in the fossil fuels. AP-42 emissions factors for the fossil fuels were also compared to the mass balance emissions factors and EPA CFR Title 40 emissions factors. Overall, the AP-42 emissions factors for the fossil fuels did not compare well with the mass balance emissions factors or the EPA CFR Title 40 emissions factors. Regression analysis of the AP-42, EPA, and mass balance emissions factors for the fossil fuels showed a significant relationship only for CO{sub 2} and SO{sub 2}. However, the regression models underestimate the SO{sub 2} emissions by 33%. These tests illustrate the importance in performing material balances around boilers to obtain the most accurate emissions levels, especially when dealing with biofuels. The EPA emissions factors were very good at predicting the mass balance emissions factors for the fossil fuels and to a lesser degree the biofuels. While the AP-42 emissions factors and EPA CFR Title 40 emissions factors are easier to perform, especially in large, full-scale systems, this study illustrated the shortcomings of estimation techniques. 23 refs., 3 figs., 8 tabs.
Reddy, T. A.; Claridge, D.; Wu, J.
1992-01-01T23:59:59.000Z
Statistical models of energy use in commercial buildings are being increasingly used not only for predicting retrofit savings but also for identifying improper operation of HVAC systems. The conventional approach involves using multiple regression...
Mining customer credit by using neural network model with logistic regression approach
Kao, Ling-Jing
2001-01-01T23:59:59.000Z
. The objective of this research was to investigate the methodologies to mine customer credit history for the bank industry. Particularly, combination of logistic regression model and neural network technique are proposed to determine if the predictive capability...
Forrest, Timothy Lee
2007-04-25T23:59:59.000Z
This thesis presents a methodology for conducting logistic regression modeling of trip and household information obtained from household travel surveys and vehicle trip information obtained from global positioning systems (GPS) to better understand...
Mining customer credit by using neural network model with logistic regression approach
Kao, Ling-Jing
2001-01-01T23:59:59.000Z
. The objective of this research was to investigate the methodologies to mine customer credit history for the bank industry. Particularly, combination of logistic regression model and neural network technique are proposed to determine if the predictive capability...
Real-time semiparametric regression BY J. LUTS1, T. BRODERICK2 AND M.P. WAND1
Wand, Matt
regression refers to a large class of regression models that provide for non-linear predictor effects using regression is quite broad and includes, as special cases, generalized linear mixed models, generalizedReal-time semiparametric regression BY J. LUTS1, T. BRODERICK2 AND M.P. WAND1 1 School
Principle of Least Squares Regression Equations Residuals Correlation and Regression
Watkins, Joseph C.
Principle of Least Squares Regression Equations Residuals Topic 3 Correlation and Regression Linear Regression I 1 / 15 #12;Principle of Least Squares Regression Equations Residuals Outline Principle of Least Squares Regression Equations Residuals 2 / 15 #12;Principle of Least Squares Regression Equations
Combining Regression Trees and Radial Basis Function Networks Mark Orr, John Hallam,
Edinburgh, University of
a model using linear regression. The non-linear transformation is controlled by a set of m basis functions, 1988] transform the n- dimensional inputs non-linearly to an m-dimensional space and then estimate and radii and the second estimates the weights, fw j g m j=1 , of the linear regression model f(x) = m X j=1
Robust Linearization of RF Amplifiers Using NonLinear Internal Model Control Method
Paris-Sud XI, Université de
Robust Linearization of RF Amplifiers Using NonLinear Internal Model Control Method Smail Bachir #1, the nonlinear Internal Model Control (IMC) method is introduced and applied to linearize high frequency Power to be controlled [8]. If the model is a perfect representation of the non linear system, the controller can
Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model
Edakunni, Narayanan U.; Schaal, Stefan; Vijayakumar, Sethu
2006-01-01T23:59:59.000Z
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this
Verleysen, Michel
regression (PCR) and partial least squares regression (PLSR). Then, we will propose to incorporate non-linearChemometric calibration of infrared spectrometers: selection and validation of variables by non-linear (step by step) for the selection of spectral variables, using linear regression or neural networks
Regression with Missing X's: A Review
Roderick J. A. Little
2011-01-01T23:59:59.000Z
1968), "Missing Data in Regression Analysis," Journal of theTechnometrics, Little: Regression With Missing X's Ibrahim,Influence Multiple Linear Regression with Incomplete Data,"
Katipamula, S.; Reddy, T. A.; Claridge, D. E.
1994-01-01T23:59:59.000Z
An empirical or regression modeling approach is simple to develop and easy to use compared to use of detailed hourly simulations. Therefore, regression analysis has become a widely used tool in the determination of annual energy savings accruing...
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
GENERALIZED LINEAR MODELS WITH REGULARIZATION A DISSERTATION
Hastie, Trevor
GENERALIZED LINEAR MODELS WITH REGULARIZATION A DISSERTATION SUBMITTED TO THE DEPARTMENT Park 2006 All Rights Reserved ii #12;I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor
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
Gas Plume Species Identification in LWIR Hyperspectral Imagery by Regression Analyses
Salvaggio, Carl
of the algorithm is a stepwise linear regression technique that only includes a basis vector in the model such as atmospheric compensation, gas absorption and emission, background modeling, and fitting a linear regression to a non-linear radiance model were addressed in order to generate the matrix of basis vectors. Synthetic
Helices in High Dimensional Regression
Ker-Chau Li
2011-01-01T23:59:59.000Z
of "Sliced inverse regression." J. Amer. Statist. Assoc. 86C. (1992). Measurement error regression with unknown link:a projection-pursuit type regression model. Ann. Statist. 19
RegTools: A Julia Package for Assisting Regression Analysis
Liang, Muzhou
2015-01-01T23:59:59.000Z
and R. E. Welsch. 1980. ”Regression Diagnostics: IdentifyingObservation in Linear Regression. ” Techometrics 19:15-18. [Observations in Linear Regression. ” Journal of the American
Nonlinear regression Gordon K. Smyth
Smyth, Gordon K.
Nonlinear regression Gordon K. Smyth Volume 3, pp 14051411 in Encyclopedia of Environmetrics (ISBN, 2002 #12;Nonlinear regression The basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response Y to a vector of predictor variables x D x1, . . . , xk T (see Linear
A Library for Locally Weighted Projection Regression --Supplementary Documentation --
problems: · The function to be learnt is non-linear. Otherwise having multiple local models is a waste of resources, and you should rather use ordinary linear regression, or partial least squares (PLS) for the caseA Library for Locally Weighted Projection Regression -- Supplementary Documentation -- Stefan
Directed Regression Stanford University
Van Roy, Ben
Directed Regression Yi-hao Kao Stanford University Stanford, CA 94305 yihaokao Stanford, CA 94305 xyan@stanford.edu Abstract When used to guide decisions, linear regression analysis typically involves esti- mation of regression coefficients via ordinary least squares and their subsequent
Blood Glucose Level Prediction using Physiological Models and Support Vector Regression
Bunescu, Razvan C.
Blood Glucose Level Prediction using Physiological Models and Support Vector Regression Razvan continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking
Sukhatme, Gaurav S.
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking sensor data to energy expenditure is the ques- tion of normalizating across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative
Top-Down Induction of Model Trees with Regression and Splitting Nodes
Ceci, Michelangelo
has been extended in GUIDE [12] and SECRET [3]. All these systems perform a top-down inductionTop-Down Induction of Model Trees with Regression and Splitting Nodes Donato Malerba, Member, IEEE with the leaves. A different approach is followed in SUPPORT and SECRET, which reduce the computational complexity
COMPSTAT'2004 Symposium c Physica-Verlag/Springer 2004 ROBUST REGRESSION QUANTILES WITH
to robustly estimate linear regression quantiles with censored data. We adjust the estimator recently points. 1 Introduction We consider the linear regression setting, in which we have to model the re, the deepest regression estimator [5], which has been defined for non censored data. In Section 4 we
Grouping Entities in a Fleet by Community Detection in Network of Regression Models
Pansari, Pankaj; Sundararajan, Ramasubramanian
2015-01-01T23:59:59.000Z
This paper deals with grouping of entities in a fleet based on their behavior. The behavior of each entity is characterized by its historical dataset, which comprises a dependent variable, typically a performance measure, and multiple independent variables, typically operating conditions. A regression model built using this dataset is used as a proxy for the behavior of an entity. The validation error of the model of one unit with respect to the dataset of another unit is used as a measure of the difference in behavior between two units. Grouping entities based on their behavior is posed as a graph clustering problem with nodes representing regression models and edge weights given by the validation errors. Specifically, we find communities in this graph, having dense edge connections within and sparse connections outside. A way to assess the goodness of grouping and finding the optimum number of divisions is proposed. The algorithm and measures proposed are illustrated with application to synthetic data.
Linear and NonLinear Estimation Methods Applied to the Hemodynamic model
Schaal, Stefan
Linear and NonLinear Estimation Methods Applied to the Hemodynamic model Evangelos A. Theodorou s that controls the blood inflow. The total balloon model can be defined by the 4 differential equations the hemodynamic process of the balloon model. These equations consist of a set of deterministic highly non
Li, X.; Baltazar, J. C.
2013-01-01T23:59:59.000Z
Cooling Energy Consumption in Large Commercial Buildings. ASME/JSME/JSES International Solar Energy Conference, San Francisco, California, March, pp. 307-322. Katipamula, S., Reddy, T. A., Claridge, D.E., 1998, Multivariate Regression Modeling...-Carlos Baltazar, Ph.D., P.E. Research Engineering Associate Research Engineer Energy Systems Laboratory, Texas A&M Engineering Experiment Station The Texas A&M University System, College Station, TX, 77845 ABSTRACT Whole-building energy savings...
Li, Ke
2012-02-14T23:59:59.000Z
of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2010 Major Subject: Agricultural Economics Essays on Regression Spline Structural Nonparametric Stochastic Production Frontier Estimation and Ine ciency Analysis Models Copyright 2010 Ke Li... of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Ximing Wu Committee Members, David Bessler H. Alan Love Qi Li Head of Department, John P. Nichols December 2010 Major Subject: Agricultural Economics iii ABSTRACT...
Spectral learning of linear dynamics from generalised-linear observations
a non-linear and non-Gaussian observation process. We use this approach to obtain estimates to the generalised-linear regression model [8]), where the expected value of an observation is given by a monotonicSpectral learning of linear dynamics from generalised-linear observations with application
Wang, J.; Claridge, D. E.
1998-01-01T23:59:59.000Z
Regression models of measured energy use in buildings are widely used as baseline models to determine retrofit savings from measured energy consumption. It is less expensive to determine savings from monthly utility bills when they are available...
SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION
Kaplan, David M.; Sun, Yixiao
2012-01-01T23:59:59.000Z
Instrumental quantile regression inference for structuralsample inference for quantile regression models. Journal ofmethods for median regression models. Econometrica,
Kissock, J. K.; Haberl, J. S.; Claridge, D. E.
2002-11-01T23:59:59.000Z
This report summarizes the results of ASHRAE Research Project 1050: Development of a Toolkit for Calculating Linear, Change-Point Linear and Multiple Linear Inverse Building Energy Analysis Models. The Inverse Modeling Toolkit (WIT) is a FORTRAN 90...
Jan de Leeuw
2011-01-01T23:59:59.000Z
values. And then we perform an ordinary monotone regression.nally mention that monotone regression can be generalized inMONOTONE REGRESSION JAN DE LEEUW Abstract. This is an entry
Datadriven calibration of linear estimators with minimal penalties
This paper tackles the problem of selecting among several linear estimators in non parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge classification, with linear and non linear predictors [37, 36]. A central issue common to all regularization
Data-driven calibration of linear estimators with minimal penalties
Paris-Sud XI, Université de
This paper tackles the problem of selecting among several linear estimators in non- parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge classification, with linear and non- linear predictors [37, 36]. A central issue common to all regularization
Efficient Semiparametric Estimators for Nonlinear Regressions and Models under Sample Selection Bias
Kim, Mi Jeong
2012-10-19T23:59:59.000Z
(X; ) + ; where Y 2 R is the response variable, X 2 Rk is the predictor variable, 2 Rp is the unknown regression parameter and is the random error satisfying E( jX) = 0 and E( 2jX) = 2. Y and are assumed to have nite fourth moments. The parameter vector...-dimensional parameter and the model error satis es the usual mean zero assumption E( jX) = 0. In addition, they also assumed that has a constant yet unknown variance 2, that is, E( 2jX) = 2. The observa- tions are denoted (X1; Y1); : : : ; (Xn; Yn), each satis...
Automatic Digital Surface Model (DSM) Generation from Linear Array Images
Giger, Christine
Automatic Digital Surface Model (DSM) Generation from Linear Array Images A dissertation submitted-examiner Presented by Li Zhang Zurich 2005 #12;IGP Mitteilungen Nr. 88 Automatic Digital Surface Model (DSM-906467-55-4 #12;DISS. ETH NO. 16078 Automatic Digital Surface Model (DSM) Generation from Linear Array Images
A general approach to statistical modeling of physical laws: nonparametric regression
I. Grabec
2007-04-01T23:59:59.000Z
Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup. A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance between data points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.
Error Control of Iterative Linear Solvers for Integrated Groundwater Models
Dixon, Matthew; Brush, Charles; Chung, Francis; Dogrul, Emin; Kadir, Tariq
2010-01-01T23:59:59.000Z
An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient (PCG) method or Generalized Minimum RESidual method (GMRES) is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of 'forward error bound estimation' to show how rescaling the linear system affects the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed using the USGS GSFLOW package and the California State Department of Water Resources' Integrated Water Flow Model (IWFM), we observe that this error bound guides the choice of a prac...
General model selection estimation of a periodic regression with a Gaussian noise
Konev, Victor; 10.1007/s10463-008-0193-1
2010-01-01T23:59:59.000Z
This paper considers the problem of estimating a periodic function in a continuous time regression model with an additive stationary gaussian noise having unknown correlation function. A general model selection procedure on the basis of arbitrary projective estimates, which does not need the knowledge of the noise correlation function, is proposed. A non-asymptotic upper bound for quadratic risk (oracle inequality) has been derived under mild conditions on the noise. For the Ornstein-Uhlenbeck noise the risk upper bound is shown to be uniform in the nuisance parameter. In the case of gaussian white noise the constructed procedure has some advantages as compared with the procedure based on the least squares estimates (LSE). The asymptotic minimaxity of the estimates has been proved. The proposed model selection scheme is extended also to the estimation problem based on the discrete data applicably to the situation when high frequency sampling can not be provided.
Galtchouk, Leonid
2008-01-01T23:59:59.000Z
An adaptive nonparametric estimation procedure is constructed for the estimation problem of heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (an oracle inequality) is constructed.
Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models
Galtchouk, Leonid
2010-01-01T23:59:59.000Z
An adaptive nonparametric estimation procedure is constructed for heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (oracle inequality) is obtained
Pazzani, Michael J.
this insightful or advise anyone to act upon this model. If a baseball player interested in maximizing his income guide future decision-making. For example, many lenders use a credit score to help determine whether to make a loan. This score is a combination of many factors such as income, debt, and past payment history
Factoring Gaussian Precision Matrices for Linear Dynamic Models
Frankel, Joe; King, Simon
2007-01-01T23:59:59.000Z
The linear dynamic model (LDM), also known as the Kalman filter model, has been the subject of research in the engineering, control, and more recently, machine learning and speech technology communities. The Gaussian noise processes are usually...
MULTIVARIATE NONPARAMETRIC REGRESSION AND VISUALIZATION
Klemelä, Jussi
not be available in electronic format. Library of Congress Cataloging-in-Publication Data: Klemel¨a, Jussi AND CLASSIFICATION 1 Overview of Regression and Classification 3 2 Linear Methods and Extensions 77 3 Kernel Methods Visualization xxi I.4 Literature xxiii PART I METHODS OF REGRESSION AND CLASSIFICATION 1 Overview of Regression
Linear programming model for optimum resource allocation in rural systems
Devadas, V. [Indian Inst. of Tech., Kharagpur (India)
1997-07-01T23:59:59.000Z
The article presents a model for optimum resource allocation in a rural system. Making use of linear programming, the objective function of the linear programming model is to maximize the revenue of the rural system, and optimum resource allocation is made subject to a number of energy- and nonenergy-related constraints relevant to the rural system. The model also quantifies the major yields as well as the by-products of different sectors of the rural economic system.
Testing Generalized Linear Models Using Smoothing Spline Methods
Wang, Yuedong
the hypothesis of Generalized Linear Models (GLM) versus general smoothing spline models for data from exponential families. The tests developed are based on the connection between the smoothing spline models and residual plots are less informative for discrete data. Therefore general diagnostic and model building
Estimation of the linear-plateau segmented regression model in the presence of measurement error
Grimshaw, Scott D.
1985-01-01T23:59:59.000Z
/c?) 4(~/o ) I (2-6) where 4(') is the standard normal density. Hence, letting = /m(YW)/o?, (2. 5) can be written as V [1 f fx(t) (( ) dz dt + f? j fx(t) 4( ) dz dt] m As the number of repeated observations is increased, 1im P [misclassif ication...] = / [lim C'(v )] f (t) dt + P [lim m(-v ) ] f (t) dt m x m x m~ by Lease B. l, = 0, since lim @(v ) = @(- ) for t & Y , m lim @(-v ) = @(~) for t & Y m Therefore, in the limit, the probability of misclassification is zero. When the join point, Y...
Open source software maturity model based on linear regression and Bayesian analysis
Zhang, Dongmin
2009-05-15T23:59:59.000Z
Open Source Software (OSS) is widely used and is becoming a significant and irreplaceable part of the software engineering community. Today a huge number of OSS exist. This becomes a problem if one needs to choose from such a large pool of OSS...
Estimation of the linear-plateau segmented regression model in the presence of measurement error
Grimshaw, Scott D.
1985-01-01T23:59:59.000Z
u continuous density f , with E(x. ) & , ei - N(0, c ), and x i ' i e with x, , e. , u. . independent. 1 1 13 Y = [ Yl Y2 Y ]' (3. 2) 12 Zl Z2 Z a0 al ]' [ el e2 en] ' [ w) w2 wn' Z. =X. 1 1. if X &Y (3. 3) and if X. ) Y , i=if /n 1 m 3...-Y) III( ) ) ~m ? [E(w. . )] 2 i3 (iv) E(w, . ) = 3a P(X, ( y) 4 4 13 U 1 ~ + J (t-v) ~( ) f (t) ? Y m x + f (t-v) o(-v ) ?? + ? f (t-Y) 4(& ) f?(t) dt ~m f? (t-Y) 4( ) t?(t) dt ?m + 6~ f (t-v) @(? ) t (t) dt U Y m x + 3(2m-1)( ) f (t-v) 4...
Rothermel, Gregg
Regression testing is an important activity that can account for a large proportion of the cost of software testing technique that (1) chooses a subset of a test suite that was used to test the software before the modifications, and then (2) uses this subset to test the modified software. Selective regression testing
Robust Minimax Probability Machine Regression Robust Minimax Probability Machine Regression
Grudic, Greg
Robust Minimax Probability Machine Regression Robust Minimax Probability Machine Regression Thomas of Computer Science University of Colorado Boulder, C0 80309-0430, USA Abstract We formulate regression as maximizing the minimum probability () that the regression model is within ± of all future observations (i
Non-linear transformer modeling and simulation
Archer, W.E.; Deveney, M.F.; Nagel, R.L.
1994-08-01T23:59:59.000Z
Transformers models for simulation with Pspice and Analogy`s Saber are being developed using experimental B-H Loop and network analyzer measurements. The models are evaluated for accuracy and convergence using several test circuits. Results are presented which demonstrate the effects on circuit performance from magnetic core losses eddy currents and mechanical stress on the magnetic cores.
Error Control of Iterative Linear Solvers for Integrated Groundwater Models
Bai, Zhaojun
gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance for integrated groundwater models, which are implicitly coupled to another model, such as surface water models the correspondence between the residual error in the preconditioned linear system and the solution error. Using
Data-driven calibration of linear estimators with minimal Sylvain Arlot
This paper tackles the problem of selecting among several linear estimators in non- parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge, with linear and non-linear predictors [19, 18]. A central issue common to all regularization frameworks
Nonlinear regression analysis of field emission data
Barry, Scott Wilson
1992-01-01T23:59:59.000Z
for the zirconium/tungsten cathode data. . Regressed enhancement fa. ctors using the integral model(solid line) and approximate model(dashed line) over a range of fixed work functions for the zirconium/tungsten cathode data. 70 Integral model(solid line...) and linear( dashed line) fitting curves for the zirconium/tungsten cathode data, . 71 33 Integral model(solid line) and linear(dashed line) fitting curves for the zirconium/tungsten cathode data, excluding the last three suspect data points. 72 CHAPTER I...
Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches
Yoo S.; Yang, Y.; Carbonell, J.
2011-10-24T23:59:59.000Z
Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.
Baker, Jack W.
Regression models for predicting the probability of near-fault earthquake ground motion pulses to the earthquake magnitude, but other predictive parameters are also considered and discussed. Both empirical University, Stanford, CA, USA ABSTRACT: Near-fault earthquake ground motions containing large velocity pulses
.8, a positive predictive value of 27.5% and a negative predictive value of 99.4%. CONCLUSIONS: The logisticThe use of a new logistic regression model for predicting the outcome of pregnancies of unknown, London UK. E-mail: gcondous@hotmail.com BACKGROUND: The aim of this study was to generate and evaluate
On Robust Regression in Photogrammetric Point Clouds
Schindler, Konrad
On Robust Regression in Photogrammetric Point Clouds Konrad Schindler and Horst Bischof Institute,bischof}@icg.tu-graz.ac.at Abstract. Many applications in computer vision require robust linear regression on photogrammetrically for robust regression are based on distance measures from the regression surface to the points
FAST SPEAKER ADAPTION VIA MAXIMUM PENALIZED LIKELIHOOD KERNEL REGRESSION
Tsang Wai Hung "Ivor"
of MLLR using non- linear regression. Specifically, kernel regression is applied with appropriate of Science and Technology Clear Water Bay, Hong Kong ABSTRACT Maximum likelihood linear regression (MLLR) has], and transformation-based methods, most notably, maximum likelihood linear regression (MLLR) adap- tation [3]. However
Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees
Anderson, Charles H.
, kernel types, and platforms. 1. INTRODUCTION Due to power consumption and heat dissipation concerns for non-linear regression can be used to estimate timing models from data, capturing the best of both ap
Integer linear programming models for a cement delivery problem
Hertz, Alain
Integer linear programming models for a cement delivery problem Alain Hertz D´epartement de math.uldry@unifr.ch and marino.widmer@unifr.ch April 4, 2011 Abstract We consider a cement delivery problem with an heterogeneous in [14], [15] and [16] and are reviewed in [4]. In this paper, we study a cement delivery problem which
GENERALIZED LINEAR MODELING APPROACH TO STOCHASTIC WEATHER GENERATORS
Katz, Richard
) Multisites (Spatial dependence of daily weather) -- Software R open source statistical programming language (Capable of "reproducing" any desired statistic) -- Disadvantages Synthetic weather looks too much like") Not amenable to uncertainty analysis #12;#12;#12;(2) Generalized Linear Models · Statistical Framework
Model checking LTL over controllable linear systems is decidable
Pappas, George J.
Model checking LTL over controllable linear systems is decidable Paulo Tabuada and George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA 19104 {tabuadap,pappasg}@seas.upenn.edu Abstract. The use of algorithmic verification and synthesis tools for hy- brid systems is currently limited
Bootstrap for model selection: linear approximation of the optimism
Verleysen, Michel
Bootstrap for model selection: linear approximation of the optimism G. Simon1 , A. Lendasse2 , M. Lemaître 4, B-1348 Louvain-la-Neuve, Belgium, lendasse@auto.ucl.ac.be Abstract. The bootstrap resampling, as artificial neural networks. Nevertheless, the use of the bootstrap implies a high computational load
GROUP SPARSITY VIA LINEAR-TIME PROJECTION
2008-08-01T23:59:59.000Z
Jul 31, 2008 ... linear regression model subject to a bound on the l1-norm of the coefficients; .... this strategy scales poorly with the number of non-zero groups.
Slicing Regression: Dimension Reduction via Inverse Regression
Ker-Chau Li
2011-01-01T23:59:59.000Z
Semiparametric Additive Regression," unpublished manuscript.N . , and L i , K . C. (in press), "Slicing Regression: aLink-Free Regression Method," The Annals of Statistics.
Ferreira, Márcia M. C.
to be adequate for solving this problem. Besides, PLS allows limited modeling of non-linear relations by using new alternative to the existing linear and non-linear multivariate calibra- tion approaches and structural risk minimization. SVM is able to treat both, linear and non- linear data sets and control or even
Regression Given input data (features), predict value of a
Giger, Christine
· The complete graph Non-linear regression #12;· Need to fit non-linear functions · example: polynomials Non-linear of the inputs · After applying z(x), we fit a plane in 3D-space Non-linear regression y = 0 + 1x + 2x2 z(x) = 1 x x2 #12;x y x2 regression from x to y, non-linear lifting from x to z=[x x2] regression from [x x2
Variable selection using Adaptive Non-linear Interaction Structures in High dimensions
Radchenko, Peter
superior predictive performance over other approaches. Some key words: Non-Linear Regression; InteractionsVariable selection using Adaptive Non-linear Interaction Structures in High dimensions Peter a tra- ditional linear regression model in which the number of predictors, p, is large relative
Efficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors
Vijayakumar, Sethu
a linear logistic regression as the base classifier with Bayesian learning for the regression The Randomly Varying Coefficient model approximates a multivariate non-linear function using a set of localEfficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors Narayanan
Bardsley, John
as a cancer risk. In the United States, EPA sets guidelines specifying upper limits on the amount of exposure groups than low exposure. The objective of regression analysis is to estimate the rate of cancer deaths cases or deaths attributable to cancer) using a number of explanatory variables believed to be related
Regression analysis for peak designation in pulsatile pressure signals
Scalzo, Fabien; Xu, Peng; Asgari, Shadnaz; Bergsneider, Marvin; Hu, Xiao
2009-01-01T23:59:59.000Z
5 ORIGINAL ARTICLE Regression analysis for peak designationwith more versatile regression models. The experimentalof different state-of-the-art regression analysis methods is
Bayesian Analysis of Curves Shape Variation through Registration and Regression
Telesca, Donatello
2015-01-01T23:59:59.000Z
Wehrly (1986). Kernel regression estimation using repeatedadaptive Bayesian penalized regression splines (P-splines).self-modeling nonlinear regression. The Annals of Statistics
Holographic transports and stability in anisotropic linear axion model
Xian-Hui Ge; Yi Ling; Chao Niu; Sang-Jin Sin
2015-01-15T23:59:59.000Z
We study thermoelectric conductivities and shear viscosities in a holographically anisotropic model. Momentum relaxation is realized through perturbing the linear axion field. AC conductivity exhibits a conherent/incoherent metal transition. The longitudinal shear viscosity for prolate anisotropy violates the bound conjectured by Kovtun-Son-Starinets. We also find that thermodynamic and dynamical instabilities are not always equivalent, which provides a counter example of the Gubser-Mitra conjecture.
Goodness-of-Fit Test Issues in Generalized Linear Mixed Models
Chen, Nai-Wei
2012-02-14T23:59:59.000Z
Linear mixed models and generalized linear mixed models are random-effects models widely applied to analyze clustered or hierarchical data. Generally, random effects are often assumed to be normally distributed in the ...
A Linear Circuit Model For Social Influence Analysis
Xiang, Biao; Liu, Qi; Xiong, Hui
2012-01-01T23:59:59.000Z
Understanding the behaviors of information propagation is essential for the effective exploitation of social influence in social networks. However, few existing influence models are both tractable and efficient for describing the information propagation process and quantitatively measuring social influence. To this end, in this paper, we develop a linear social influence model, named Circuit due to its close relation to the circuit network. Based on the predefined four axioms of social influence, we first demonstrate that our model can efficiently measure the influence strength between any pair of nodes. Along this line, an upper bound of the node(s)' influence is identified for potential use, e.g., reducing the search space. Furthermore, we provide the physical implication of the Circuit model and also a deep analysis of its relationships with the existing methods, such as PageRank. Then, we propose that the Circuit model provides a natural solution to the problems of computing each single node's authority a...
Jet propagation within a Linearized Boltzmann Transport Model
Luo, Tan; Wang, Xin-Nian; Zhu, Yan
2015-01-01T23:59:59.000Z
A Linear Boltzmann Transport (LBT) model has been developed for the study of jet propagation inside a quark-gluon plasma. Both leading and thermal recoiled partons are transported according to the Boltzmann equations to account for jet-induced medium excitations. In this talk, we present our study within the LBT model in which we implement the complete set of elastic parton scattering processes. We investigate elastic parton energy loss and their energy and length dependence. We further investigate elastic energy loss and transverse shape of reconstructed jets. Contributions from the recoiled thermal partons are found to have significant influences on the jet energy loss and transverse profile.
Jet propagation within a Linearized Boltzmann Transport Model
Tan Luo; Yayun He; Xin-Nian Wang; Yan Zhu
2015-06-12T23:59:59.000Z
A Linear Boltzmann Transport (LBT) model has been developed for the study of jet propagation inside a quark-gluon plasma. Both leading and thermal recoiled partons are transported according to the Boltzmann equations to account for jet-induced medium excitations. In this talk, we present our study within the LBT model in which we implement the complete set of elastic parton scattering processes. We investigate elastic parton energy loss and their energy and length dependence. We further investigate elastic energy loss and transverse shape of reconstructed jets. Contributions from the recoiled thermal partons are found to have significant influences on the jet energy loss and transverse profile.
Liu, Yufeng
selection; RKHS; Semiparametric regression; Shrinkage; Smoothing splines. 1. INTRODUCTION Linear to be linear and others to be non- linear. Partially linear models have wide applications in practice due://pubs.amstat.org. Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models Hao Helen ZHANG, Guang
Minimum Description Length Model Selection Criteria for Generalized Linear Models Mark Hansen
Yu, Bin
of simulations for logistic regression illustrate that mixture MDL can ``bridge'' AIC and BIC in the sense. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework (cf for a probability distribution Q on A. An integervalued function L corresponds to the code length of a binary
Chen, Wei-Chen [ORNL; Maitra, Ranjan [Iowa State University
2011-01-01T23:59:59.000Z
We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithm (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.
Dignum, David Rory
1988-01-01T23:59:59.000Z
THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK MILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE CHARACTERISTICS A Thesis by DAVID RORY DIGNUM Submitted to the Graduate College of Texas Afdi University... in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 1988 Maj or Subj cot: Forestry THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK WILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE...
Bayesian Methods in Nutrition Epidemiology and Regression-based Predictive Models in Healthcare
Zhang, Saijuan
2012-02-14T23:59:59.000Z
This dissertation has mainly two parts. In the first part, we propose a bivariate nonlinear multivariate measurement error model to understand the distribution of dietary intake and extend it to a multivariate model to capture dietary patterns...
Al-Arfaj, Muhammad A.
compares the closed-loop performance of three control structures using an approximate linear model. Responses based on the linear model for various control structures show a good agreement when compared of the linear model is shown to be better in a single-end control system than in a dual-end control system
Jan de Leeuw
2011-01-01T23:59:59.000Z
Stone. Classi?cation and Regression Trees. Wadsworth, 1984.Projection Pursuit Regression. Journal of the AmericanK.C. Li. Sliced Inverse Regression for Dimension Reduction (
Gerber, Samuel
2013-01-01T23:59:59.000Z
1984). Classification and Regression Trees. Monterey, CA:Piecewise-polynomial regression trees. Statistica Sinica 4,BART: Bayesian additive regression trees. Ann. Appl. Stat.
Steam-circuit Model for the Compact Linear Fresnel Reflector , G. L. Morrison1
Steam-circuit Model for the Compact Linear Fresnel Reflector Prototype J. D. Pye1 , G. L. Morrison1.pye@student.unsw.edu.au Abstract The Compact Linear Fresnel Reflector (CLFR) is a linear-concentrating solar thermal energy system The Compact Linear Fresnel Reflector (CLFR) was first conceived of in 1992-1993 and was patented in 1995
MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification
Murphy, Robert F.
Allocation (LDA) (Blei et al., 2003) is an example of such models for textual documents. LDA posits that each stud- ied in text modeling (McCallum et al., 2006) and im- age analysis (Blei & Jordan, 2003). Recently, super- vised variants of LDA have been proposed, including the supervised LDA (sLDA) (Blei & Mc
Regression Given input data (features), predict value of a
Giger, Christine
^ = (XX> ) 1 Xy etc. #12;! · Many relations are not linear · The complete graph Non-linear regression #12;! · Need to fit non-linear functions · example: polynomials Non-linear regression y = 6 10000 x5 82 10000 x) the dimension of the inputs · After applying z(x), we fit a plane in 3D-space Non-linear regression y = 0 + 1x
A Formulation for Minimax Probability Machine Regression
Grudic, Greg
A Formulation for Minimax Probability Machine Regression Thomas Strohmann Department of Computer Science University of Colorado, Boulder grudic@cs.colorado.edu Abstract We formulate the regression of the regression model will be within some ± bound of the true regression function. Our formulation is unique
Wang, J.; Claridge, D. E.
1998-01-01T23:59:59.000Z
the annual prediction error to 0.6% from -6.1 % . The modified heating regression models reduces the annual prediction error to 4.1% from 5.7%. Ec=6.6569+0.1 875(67.044-Tdb)'+0.6756(Tdb-67.044)7 Eh=0.909 1 -.3662(67.04-Tdb)'-.0462(Tdb-67.044)' Ec=5....8505+. 1736(67.7082-Tdb)*+.6794(Tdb-67.708)' ~h=0.97 18-0.341 (67.044-~db)'-0.0458(~db-67.044)+ CONCLUSIONS The results of the four cases studied indicate that when the AHUs operate 24 hours per day, the annual prediction error of the regular cooling...
Forrest, Timothy Lee
2007-04-25T23:59:59.000Z
...................................................................................................... 47 7 Significance Levels of Variables in Each Model......................................... 49 A-1 Example of Household Data File Format (Laredo) ..................................... 60 A...-2 Example of Household Data File Format Codes (Laredo) .......................... 62 A-3 Example of Person Data File Format (Laredo)............................................ 63 A-4 Example...
A spatiotemporal auto-regressive moving average model for solar radiation
Glasbey, Chris
.J. Allcroft Biomathematics and Statistics Scotland King's Buildings, Edinburgh, EH9 3JZ, Scotland January 11, 2008 Abstract To investigate the variability in energy output from a network of photo-voltaic cells, so realisations of energy output. Key words: clearness index, Matern process, spatio-temporal models, Toeplitz
Exploiting separability in large-scale linear support vector machine ...
2009-04-20T23:59:59.000Z
Aug 7, 2007 ... universum classification, ordinal regression and ?-insensitive regression. .... ear, quadratic and non-linear optimization programmes.
A DISTRIBUTION WITH GIVEN MARGINALS AND GIVEN REGRESSION CURVE
Cuadras, Carles M.
; and the possibility of using this construction to test nonlinear regression procedures and methods of estimation; mixture of distributions; nonlinear regression; extremal correla tions. AMS(1991) subject classification this construction to the nonlinear case. If ' is a monotone nonlinear function, satisfying some restrictions (e
Limited Model Information Control Design for Linear Discrete-Time Systems with Stochastic Parameters
Johansson, Karl Henrik
Limited Model Information Control Design for Linear Discrete-Time Systems with Stochastic systems with stochastically varying parameters. Recently, there have been studies in optimal control subsystems' parameters. There have been many studies in optimal control design for linear discrete
Huang, Su-Yun
Multiclass Support Vector Classification via Regression Multiclass Support Vector Classification via Regression Pei-Chun Chen peichun@stat.sinica.edu.tw Institute of Statistical Science Academia classification is considered and resolved through the mul- tiresponse linear regression approach. Scores are used
Cooling Energy Demand Evaluation by Meansof Regression Models Obtained From Dynamic Simulations
Catalina, T.; Virgone, J.
2011-01-01T23:59:59.000Z
was calculated to be -8.78oC (Moscow in January) and maximum of 42.9 oC (Abu-Dhabi in August). The hourly values of outdoor air temperature and solar radiation were obtained using Trnsys (Trnsys, 2006) meteonorm files. b) Glazing surface and distribution... the ,,black-box,, function, dynamic simulations were conducted using Trnsys 16 software (Trnsys, 2005). The Trnsys building model, known as, Type 56, is compliant with general requirements of European Directive on the energy performance of buildings...
Modelling the e#ects of air pollution on health using Bayesian Dynamic Generalised Linear Models
Bath, University of
Modelling the e#ects of air pollution on health using Bayesian Dynamic Generalised Linear Models 1 Introduction The potential detrimental e#ects of ambient air pollution is a major issue in public (2004)). Large multicity studies such as `Air pollution and health: a European approach' (APHEA
Narumalani, S. [Nebraska Univ., Lincoln, NE (United States). Dept. of Geography; Jensen, J.R.; Althausen, J.D.; Burkhalter, S. [South Carolina Univ., Columbia, SC (United States). Dept. of Geography; Mackey, H.E. Jr. [Westinghouse Savannah River Co., Aiken, SC (United States)
1994-06-01T23:59:59.000Z
Since aquatic macrophytes have an important influence on the physical and chemical processes of an ecosystem while simultaneously affecting human activity, it is imperative that they be inventoried and managed wisely. However, mapping wetlands can be a major challenge because they are found in diverse geographic areas ranging from small tributary streams, to shrub or scrub and marsh communities, to open water lacustrian environments. In addition, the type and spatial distribution of wetlands can change dramatically from season to season, especially when nonpersistent species are present. This research, focuses on developing a model for predicting the future growth and distribution of aquatic macrophytes. This model will use a geographic information system (GIS) to analyze some of the biophysical variables that affect aquatic macrophyte growth and distribution. The data will provide scientists information on the future spatial growth and distribution of aquatic macrophytes. This study focuses on the Savannah River Site Par Pond (1,000 ha) and L Lake (400 ha) these are two cooling ponds that have received thermal effluent from nuclear reactor operations. Par Pond was constructed in 1958, and natural invasion of wetland has occurred over its 35-year history, with much of the shoreline having developed extensive beds of persistent and non-persistent aquatic macrophytes.
McAuliffe, Jon
, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area models that incorporate such nonlinearities. I. INTRODUCTION Heating, ventilation, and air-conditioning for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which
Fourth standard model family neutrino at future linear colliders
Ciftci, A.K.; Ciftci, R.; Sultansoy, S. [Physics Department, Faculty of Sciences, Ankara University, 06100 Tandogan, Ankara (Turkey); Physics Department, Faculty of Sciences and Arts, Gazi University, 06500 Teknikokullar, Ankara (Turkey)
2005-09-01T23:59:59.000Z
It is known that flavor democracy favors the existence of the fourth standard model (SM) family. In order to give nonzero masses for the first three-family fermions flavor democracy has to be slightly broken. A parametrization for democracy breaking, which gives the correct values for fundamental fermion masses and, at the same time, predicts quark and lepton Cabibbo-Kobayashi-Maskawa (CKM) matrices in a good agreement with the experimental data, is proposed. The pair productions of the fourth SM family Dirac ({nu}{sub 4}) and Majorana (N{sub 1}) neutrinos at future linear colliders with {radical}(s)=500 GeV, 1 TeV, and 3 TeV are considered. The cross section for the process e{sup +}e{sup -}{yields}{nu}{sub 4}{nu}{sub 4}(N{sub 1}N{sub 1}) and the branching ratios for possible decay modes of the both neutrinos are determined. The decays of the fourth family neutrinos into muon channels ({nu}{sub 4}(N{sub 1}){yields}{mu}{sup {+-}}W{sup {+-}}) provide cleanest signature at e{sup +}e{sup -} colliders. Meanwhile, in our parametrization this channel is dominant. W bosons produced in decays of the fourth family neutrinos will be seen in detector as either di-jets or isolated leptons. As an example, we consider the production of 200 GeV mass fourth family neutrinos at {radical}(s)=500 GeV linear colliders by taking into account di-muon plus four jet events as signatures.
Effective Models for Dark Matter at the International Linear Collider
Daniel Schmeier
2013-08-20T23:59:59.000Z
Weakly interacting massive particles (WIMPs) form a promising solution to the dark matter problem and many experiments are now searching for these particles. Using effective field theories to describe the interaction of the WIMP with the Standard Model has proven successful in providing an easy way to compare the different experimental results. In this work, we show how effective operators can be formally derived from a UV-complete underlying theory, and we analyse these operators in different experimental contexts. We put our main focus on the expected sensitivity of the International Linear Collider (ILC) in searching for WIMPs by looking at events with single photons in the final state. Furthermore, we show explicit evaluations of the relic density measurements from the Wilkinson Microwave Anisotropy Probe and the XENON Dark Matter Project direct detection measurements to compare to the expected ILC results. We find that the ILC serves as a unique tool to probe possible WIMP interactions with the Standard Model for dark matter masses below 10 GeV. This extends to masses up to 490 GeV in cases where the interaction is spin-dependent or leptophilic.
L1 Regularization Path Algorithm for Generalized Linear Models
Hastie, Trevor
of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate: ^() = argmin {- log L(y; ) + 1}, (2) where > 0 is the regularization parameter. Logistic regression with L1 the most complex stage possible. By generating the regularization path rather than computing solutions
Deepest Regression in Analytical Chemistry P. J. Rousseeuw,a
Van Aelst, Stefan
Deepest Regression in Analytical Chemistry P. J. Rousseeuw,a , S. Van Aelst,a , B. Rambalib , and J Recently the concept of regression depth has been introduced [1]. The deepest regression (DR) is a method for linear regression which is defined as the fit with the best depth relative to the data. In this paper we
REGRESSION BY SELECTING APPROPRIATE 7ROJD$\\GQDQG+$OWD\\*venir
Güvenir, H. Altay
REGRESSION BY SELECTING APPROPRIATE FEATURE(S) 7ROJD$\\GÕQDQG+$OWD\\*üvenir Department of Computer methods, called Regression by Selecting Best Feature Projection (RSBFP) and Regression by Selecting Best linear regression line on each continuous feature. In the case of categorical features, exactly one
II MODEL AND FEEDBACK LINEARIZING CONTROLLER 1 A Multilayer Perceptron Replaces a Feedback
Amaral, José Nelson
II MODEL AND FEEDBACK LINEARIZING CONTROLLER 1 A Multilayer Perceptron Replaces a Feedback Linearization Controller in a Nonlinear Servomechanism Jos'e F. Haffner, Ney T. Meyrer, Jos'e N. Amaral and Lu'is F. A. Pereira Abstract--- A Feedback Linearizing Controller (FLC) is used to train a multilayer
MODELLING OF CAVITY RECEIVER HEAT TRANSFER COMPACT LINEAR FRESNEL REFLECTOR
. This approach allows an affordable entry into renewable energy for existing coal-power producers, and allows them to meet the mandatory renewable energy targets set by the government of New South Wales . (Hu et) linear absorbers, achieving higher ground area efficiency. · Receiver is an inverted, trapezoidal, linear
Effects of the Tsallis distribution in the linear sigma model
Masamichi Ishihara
2015-04-11T23:59:59.000Z
The effects of the Tsallis distribution which has two parameters, $q$ and $T$,on physical quantities are studied using the linear sigma model in chiral phase transitions.The parameter $T$ dependences of the condensate and mass for various $q$ are shown, where $T$ is called temperature. The Tsallis distribution approaches the Boltzmann-Gibbs distribution as $q$ approaches $1$. The critical temperature and energy density are described with digamma function, and the $q$ dependences of these quantities and the extension of Stefan-Boltzmann limit of the energy density are shown. The following facts are clarified. The chiral symmetry restoration for $q>1$ occurs at low temperature, compared with the restoration at $q=1$. The sigma mass and pion mass reflect the restoration. The critical temperature decreases monotonically as $q$ increases. The small deviation from the Boltzmann-Gibbs distribution results in the large deviations of physical quantities, especially the energy density. It is displayed from the energetic point of view that the small deviation from the Boltzmann-Gibbs distribution is realized for $q>1$. The physical quantities are affected by the Tsallis distribution even when $|q-1|$ is small.
Neural network model of creep strength of austenitic stainless steels
Cambridge, University of
is a parameterised non-linear model which can be used to perform regression, in which case, a very ¯ exible, non-linear of the problems encoun- tered with linear regression. In the present study, neural network analysis was applied with a constant input set to unity. Any non-linear function can be used at the hidden units (as long
Low-order simultaneous stabilization of linear bicycle models at different forward speeds
Gundes, A. N.
Low-order simultaneous stabilization of linear bicycle models at different forward speeds A. N. G¨undes¸1 and A. Nanjangud2 Abstract-- Linear models of bicycles with rigidly attached riders, operating-track vehicles with human riders, such as bicycles, present challenging problems of modeling and control. Based
Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong
Paris-Sud XI, Université de
Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong of the dynamics of the robot and propose a new Linear Model Predictive Control scheme which is an improvement are unfortunately severely limited. Model Predictive Control, also known as Receding Horizon Control, is a general
Gonzalez, Ivan F
2008-01-01T23:59:59.000Z
Non-linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . .the system. We use non-linear regression to ?t a sinusoidalthe histogram using non-linear regression, we use analysis
Gonzalez, Ivan F.
2008-01-01T23:59:59.000Z
Non-linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . .the system. We use non-linear regression to ?t a sinusoidalthe histogram using non-linear regression, we use analysis
Results and Comparison from the SAM Linear Fresnel Technology Performance Model: Preprint
Wagner, M. J.
2012-04-01T23:59:59.000Z
This paper presents the new Linear Fresnel technology performance model in NREL's System Advisor Model. The model predicts the financial and technical performance of direct-steam-generation Linear Fresnel power plants, and can be used to analyze a range of system configurations. This paper presents a brief discussion of the model formulation and motivation, and provides extensive discussion of the model performance and financial results. The Linear Fresnel technology is also compared to other concentrating solar power technologies in both qualitative and quantitative measures. The Linear Fresnel model - developed in conjunction with the Electric Power Research Institute - provides users with the ability to model a variety of solar field layouts, fossil backup configurations, thermal receiver designs, and steam generation conditions. This flexibility aims to encompass current market solutions for the DSG Linear Fresnel technology, which is seeing increasing exposure in fossil plant augmentation and stand-alone power generation applications.
Local Dimensionality Reduction for Non-Parametric Regression
Hoffmann, Heiko; Schaal, Stefan; Vijayakumar, Sethu
2009-01-01T23:59:59.000Z
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many ...
Choquistic Regression: Generalizing Logistic Regression Using the Choquet Integral
Hüllermeier, Eyke
Choquistic Regression: Generalizing Logistic Regression Using the Choquet Integral Ali Fallah, called choquistic regression, which generalizes conventional logistic regression and takes advantage regression (2) Generalization to choquistic regression (3) First experimental results 2 #12;Logistic
Toward understanding predictability of climate: a linear stochastic modeling approach
Wang, Faming
2004-11-15T23:59:59.000Z
skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information...
E-model for Transportation Problem of Linear Stochastic Fractional ...
Dr.V.Charles
2007-03-07T23:59:59.000Z
Abstract: This paper deals with the so-called transportation problem of linear stochastic fractional programming, and ... sophisticated analysis. Stochastic ... circuit board of multi-objective LSFP, algorithm to identify redundant fractional objective ...
Logistic Model Trees Niels Landwehr
Frank, Eibe
Logistic Model Trees Niels Landwehr Institute for Computer Science, University of Freiburg for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data
Logistic Model Trees + Niels Landwehr
Frank, Eibe
Logistic Model Trees + Niels Landwehr Institute for Computer Science, University of Freiburg for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data
On the Parameter Estimation of Linear Models of Aggregate Power System Loads
Cañizares, Claudio A.
1 On the Parameter Estimation of Linear Models of Aggregate Power System Loads Valery Knyazkin-- This paper addressed some theoretical and practical issues relevant to the problem of power system load, and the corresponding results are used to validate a commonly used linear model of aggre- gate power system load
Crozier, Richard Carson
2014-06-30T23:59:59.000Z
Combined electrical and structural models of five types of permanent magnet linear electrical machines suitable for direct-drive power take-off on wave energy applications are presented. Electromagnetic models were ...
Evolution Operators for Linearly Polarized Two-Killing Cosmological Models
J. Fernando Barbero G.; Daniel Gómez Vergel; Eduardo J. S. Villaseñor
2006-06-15T23:59:59.000Z
We give a general procedure to obtain non perturbative evolution operators in closed form for quantized linearly polarized two Killing vector reductions of general relativity with a cosmological interpretation. We study the representation of these operators in Fock spaces and discuss in detail the conditions leading to unitary evolutions.
Identifying and removing sources of imprecision in polynomial regression
Brauner, Neima
Identifying and removing sources of imprecision in polynomial regression Neima Braunera and removal of imprecision in polynomial regression, originating from random errors (noise) in the independent.V. Keywords: Regression; Polynomial; Precision; Noise; Collinearity 1. Introduction Mathematical modeling
Computationally Efficient Gaussian Process Changepoint Detection and Regression
Computationally Efficient Gaussian Process Changepoint Detection and Regression by Robert Conlin Changepoint Detection and Regression by Robert Conlin Grande Submitted to the Department of Aeronautics in Aerospace Engineering Abstract Most existing GP regression algorithms assume a single generative model
Regression: What's it all about?1 Andrew Gelman2
Gelman, Andrew
Regression: What's it all about?1 Andrew Gelman2 5 Jan 2015 Regression plays three different roles in applied statistics: 1. A specification the different faces of regression modeling after being asked to review the new book
Direct-Steam Linear Fresnel Performance Model for NREL's System Advisor Model
Wagner, M. J.; Zhu, G.
2012-09-01T23:59:59.000Z
This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL's System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.
Inference for Clustered Mixed Outcomes from a Multivariate Generalized Linear Mixed Model
Chen, Hsiang-Chun
2013-08-01T23:59:59.000Z
) and E(?i2t?) with their marginal expectations over X, ??1 = EX {E(?i1t)} and ??2 = EX {E(?i2t)}, which are shown in the previous subsections. In other words, the overall total-CC is ?total = KtotalN,1,2 (??1, ??2) KtotalD,1,2 (??1, ??2) . 3.2.4....2 Multivariate Generalized Linear Mixed Model . . . . . . . . . . . . . 6 2.3 Assessing Correlation in Generalized Linear Mixed Model . . . . . . . 8 2.4 Bayesian Method for the Generalized Linear Mixed Model . . . . . . 10 3. ASSESSING CORRELATION...
Mixtures of Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems
Baveja, Satinder Singh
Mixtures of Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems David dynamical systems. The primary contribution of this work is to extend the PLG to nonlinear, stochastic- proves upon traditional linear dynamical system mod- els by using a predictive representation of state
Process Modeling of Ti-6Al-4V Linear Friction Welding (LFW)
Grujicic, Mica
Process Modeling of Ti-6Al-4V Linear Friction Welding (LFW) Mica Grujicic, G. Arakere, B finite-element analysis of the linear friction welding (LFW) process is combined with the basic physical in the open literature revealed that the weld region consists of a thermo- mechanically affected zone (TMAZ
Paris-Sud XI, Université de
Controller synthesis with very simplified linear constraints in PN model Dideban A. * Zareiee M a controller. A set of linear constraints allow forbidding the reachability of specific states. The number number of control places. A systematic method for constructing very simplified controller is offered
arXiv:submit/0910499[stat.ML]11Feb2014 Online Nonparametric Regression
Rakhlin, Alexander "Sasha"
learning with squared loss and online nonparametric regression are the same. In addition to a non experts and for online linear regression. 1 Introduction Within the online regression framework, data (x1, starting with the paper of Foster [8], has been almost exclusively on finite-dimensional linear regression
Bayesian wavelet shrinkage in transformation based linear models
Ray, Shubhankar
2002-01-01T23:59:59.000Z
Most of the noise models in signal processing are either additive or multiplicative. However, the widely held wavelet shrinkage estimators for signal denoising deal only with additive noise. In this thesis, a new Bayesian wavelet shrinkage model...
Local Regression and Likelihood
Masci, Frank
Local Regression and Likelihood Clive Loader Springer #12;#12;#12;#12;#12;Preface This book, and the associated software, have grown out of the author's work in the field of local regression over the past- ods and in particular regression, for example at the level of Draper and Smith (1981). The theoretical
Lega, Joceline
Introduction AI/Regression Technology/Search Simple Equations, Deep Understanding How Topics and Alison Cornell Simple Equations, Deep Meaning #12;Introduction AI/Regression Technology/Search Who are we helps out) Qiyam Tung and Alison Cornell Simple Equations, Deep Meaning #12;Introduction AI/Regression
Paris-Sud XI, Université de
Non-linear inversion modeling for Ultrasound Computer Tomography: transition from soft to hard Marseille cedex 20, France ABSTRACT Ultrasound Computer Tomography (UCT) is an imaging technique which has experiments. Keyword: Ultrasound Computer Tomography, Inverse Born Approximation, Elliptical Projection
Lee, Yuan-Hsuan
2011-10-21T23:59:59.000Z
This dissertation focuses on issues related to fitting an optimal variance-covariance structure in multilevel linear modeling framework with two Monte Carlo simulation studies. In the first study, the author evaluated the ...
Linear Free Energy Relationships between Dissolution Rates and Molecular Modeling Energies, 2003. In Final Form: December 18, 2003 Bulk and surface energies are calculated for endmembers reported in the literature. The calculated energies also correlate with measured dissolution rates
Accelerated Iterative Method for Solving Steady Solutions of Linearized Atmospheric Models
Watanabe, Masahiro
Accelerated Iterative Method for Solving Steady Solutions of Linearized Atmospheric Models Masahiro approach, referred to as the accelerated iterative method (AIM), is developed for solving steady state, respectively. For ensuring the accelerated asymptotic convergence of iterative procedure
Liquidity Creates Money and Debt: An Intertemporal Linear Trading Post Model
Starr, Ross M.
2014-01-01T23:59:59.000Z
Intertemporal Linear Trading Post Model Tobin, J. (1980), ”of money. Hahn (1982) poses the problem for price theory inthat the existence of money poses to the theo- rist is this:
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
Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming
Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo
2013-05-23T23:59:59.000Z
This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.
Characteristics of identifying linear dynamic models from impulse response data using Prony analysis
Trudnowski, D.J.
1992-12-01T23:59:59.000Z
The purpose of the study was to investigate the characteristics of fitting linear dynamic models to the impulse response of oscillatory dynamic systems using Prony analysis. Many dynamic systems exhibit oscillatory responses with multiple modes of oscillations. Although the underlying dynamics of such systems are often nonlinear, it is frequently possible and very useful to represent the system operating about some set point with a linear model. Derivation of such linear models can be done using two basic approaches: model the system using theoretical derivations and some linearization method such as a Taylor series expansion; or use a curve-fitting technique to optimally fit a linear model to specified system response data. Prony analysis belongs to the second class of system modeling because it is a method of fitting a linear model to the impulse response of a dynamic system. Its parallel formulation inherently makes it well suited for fitting models to oscillatory system data. Such oscillatory dynamic effects occur in large synchronous-generator-based power systems in the form of electromechanical oscillations. To study and characterize these oscillatory dynamics, BPA has developed computer codes to analyze system data using Prony analysis. The objective of this study was to develop a highly detailed understanding of the properties of using Prony analysis to fit models to systems with characteristics often encountered in power systems. This understanding was then extended to develop general ``rules-of-thumb`` for using Prony analysis. The general characteristics were investigated by performing fits to data from known linear models under controlled conditions. The conditions studied include various mathematical solution techniques; different parent system configurations; and a large variety of underlying noise characteristics.
Characteristics of identifying linear dynamic models from impulse response data using Prony analysis
Trudnowski, D.J.
1992-12-01T23:59:59.000Z
The purpose of the study was to investigate the characteristics of fitting linear dynamic models to the impulse response of oscillatory dynamic systems using Prony analysis. Many dynamic systems exhibit oscillatory responses with multiple modes of oscillations. Although the underlying dynamics of such systems are often nonlinear, it is frequently possible and very useful to represent the system operating about some set point with a linear model. Derivation of such linear models can be done using two basic approaches: model the system using theoretical derivations and some linearization method such as a Taylor series expansion; or use a curve-fitting technique to optimally fit a linear model to specified system response data. Prony analysis belongs to the second class of system modeling because it is a method of fitting a linear model to the impulse response of a dynamic system. Its parallel formulation inherently makes it well suited for fitting models to oscillatory system data. Such oscillatory dynamic effects occur in large synchronous-generator-based power systems in the form of electromechanical oscillations. To study and characterize these oscillatory dynamics, BPA has developed computer codes to analyze system data using Prony analysis. The objective of this study was to develop a highly detailed understanding of the properties of using Prony analysis to fit models to systems with characteristics often encountered in power systems. This understanding was then extended to develop general rules-of-thumb'' for using Prony analysis. The general characteristics were investigated by performing fits to data from known linear models under controlled conditions. The conditions studied include various mathematical solution techniques; different parent system configurations; and a large variety of underlying noise characteristics.
Revising Regulatory Networks: From Expression Data to Linear Causal Models
Langley, Pat
network structure. However, this ignores much ex- isting knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed
NONLINEAR CONTROL OF POWER NETWORK MODELS USING FEEDBACK LINEARIZATION
Wedeward, Kevin
network can affect each other. We consider a simple model of a power system derived from singular analysis of large electric power networks is in- creasingly important as power systems become larger construct minimally complicated dynamical models of power networks as affine nonlinear control systems
Chen, Sheng
output is a linear combination of non- linear basis functions. Provided that there is a separate and linear algebra are directly applicable. Moreover by applying linear regression statistical techniques-estimator and D-optimality Model Construction using Orthogonal Forward Regression Xia Hong, Senior Member, IEEE
Linearity Improvement ofHBT-based Doherty Power Amplifiers Based on a Simple Analytical Model
Asbeck, Peter M.
model is based on linear and nonlinear components extracted from a VBIC model for Skyworks InGaP values were extracted from a device model for a Skyworks advanced InGaP/GaAs HBT, using ADS in harmonic
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Vijayakumar, Sethu
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts Georgios Petkos for adaptive motor control exist which learn the system's inverse dynamics online and use this single model;II Command Context 1 Context 2 Dynamics models Context n Control Learning Commands Switch / Mix
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Toussaint, Marc
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts Georgios Petkos for adaptive motor control exist which learn the system's inverse dynamics online and use this single model version - to appear in ICANN 2006 #12;II Command Context 1 Context 2 Dynamics models Context n Control
Alternative mixed-integer linear programming models of a maritime inventory routing problem
Grossmann, Ignacio E.
Alternative mixed-integer linear programming models of a maritime inventory routing problem Jiang is enhanced by reformulating the time assignment constraints. Next, we present a model based on event points. Sherali et al (1999) formulated a mixed-integer programming model based on a discrete time representation
Control-Oriented Linear Parameter-Varying Modelling of a Turbocharged Diesel Engine
Cambridge, University of
Control-Oriented Linear Parameter-Varying Modelling of a Turbocharged Diesel Engine Merten Jung-- In this paper, a third order nonlinear model of the airpath of a turbocharged diesel engine is derived, which and to a higher order nonlinear model suggests the validity of this approach. I. INTRODUCTION Modern diesel
Robust Constrained Model Predictive Control using Linear Matrix Inequalities \\Lambda
Balakrishnan, Venkataramanan "Ragu"
dynamical systems, such as those encountered in chemical process control in the petrochemical, pulp process models as well as many performance criteria of significance to the process industries can
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
Balakrishnan, Venkataramanan "Ragu"
, such as those encountered in chemical process control in the petrochemical, pulp and paper industries, several process models as well as many performance criteria of significance to the process industries can
A Linear Parabolic Trough Solar Collector Performance Model
Qu, M.; Archer, D.; Masson, S.
2006-01-01T23:59:59.000Z
Collector (PTSC). This steady state, single dimensional model comprises the fundamental radiative and convective heat transfer and mass and energy balance relations programmed in the Engineering Equation Solver, EES. It considers the effects of solar...
Tian, Zhen; Folkerts, Michael; Shi, Feng; Jiang, Steve B; Jia, Xun
2015-01-01T23:59:59.000Z
Monte Carlo (MC) simulation is considered as the most accurate method for radiation dose calculations. Accuracy of a source model for a linear accelerator is critical for the overall dose calculation accuracy. In this paper, we presented an analytical source model that we recently developed for GPU-based MC dose calculations. A key concept called phase-space-ring (PSR) was proposed. It contained a group of particles that are of the same type and close in energy and radial distance to the center of the phase-space plane. The model parameterized probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. For a primary photon PSRs, the particle direction is assumed to be from the beam spot. A finite spot size is modeled with a 2D Gaussian distribution. For a scattered photon PSR, multiple Gaussian components were used to model the particle direction. The direction distribution of an electron PSRs was also modeled as a 2D Gaussian distributi...
Reading list for ST 755 Topic 1: Linear mixed models
Zhang, Daowen
problems. Journal of the American Statistical Association, 72, 320340. 5. Laird, N.M. and Ware, J.H. (1982 models. Journal of the American Statistical Association 88, 925. 3. Breslow, N.E. and Lin, X. (1995 with multiple components of dispersion. Journal of the American Statistical Associ- ation 91, 10071016. 5
Boyer, Edmond
Abstract-- In order to design a model based controller availability of a linear model technique which provides reliable linear models for control design purposes. However, classical open loop operation with time varying controllers. A set of reliable linear models for control design of the pitch
A Linear Parabolic Trough Solar Collector Performance Model
Qu, M.; Archer, D.; Masson, S.
2006-01-01T23:59:59.000Z
Equation reference ' _ gq& reflected sunlight glass envelope igigSolABs Kqq ??=? && _ ' _ aq& Reflected sunlight Absorber tube iKqq ???=? && _ ' _ airq& convection outer glass envelop surface ambient air ? )( 2 ?= Fig. 7 Model... absorber tube surface Support structure TTPAkhq base )( ??=?& / (J.P.Holman 1997) P46 (Chu 1975) (J.P.Holman 1997)P302-303 ' _ gq& conduction inner glass envelope surface outer glass envelope surface ) ) 1 2 _ g g g rr Tq ?=? ?& (J...
Vibration Model Validation for Linear Collider Detector Platforms
Bertsche, Kirk; Amann, J.W.; Markiewicz, T.W.; Oriunno, M.; Weidemann, A.; White, G.; /SLAC
2012-05-16T23:59:59.000Z
The ILC and CLIC reference designs incorporate reinforced-concrete platforms underneath the detectors so that the two detectors can each be moved onto and off of the beamline in a Push-Pull configuration. These platforms could potentially amplify ground vibrations, which would reduce luminosity. In this paper we compare vibration models to experimental data on reinforced concrete structures, estimate the impact on luminosity, and summarize implications for the design of a reinforced concrete platform for the ILC or CLIC detectors.
Logistic Model Trees Niels Landwehr1,2
Frank, Eibe
Logistic Model Trees Niels Landwehr1,2 , Mark Hall2 , and Eibe Frank2 1 Department of Computer problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way
Local Regression with Meaningful Parameters Rafael A. Irizarry
Irizarry, Rafael A.
Local Regression with Meaningful Parameters Rafael A. Irizarry Abstract Local regression or loess squares. In this paper we will present a version of local regression that fits more general parametric, and financial data are included. KEY WORDS: Local Regression, Harmonic Model, Meaningful Parameters, Sound
On Some Models in Linear Thermo-Elasticity with Rational Material Laws
Santwana Mukhopadhyay; Rainer Picard; Sascha Trostorff; Marcus Waurick
2014-09-03T23:59:59.000Z
We shall consider some common models in linear thermo-elasticity within a common structural framework. Due to the flexibility of the structural perspective we will obtain well-posedness results for a large class of generalized models allowing for more general material properties such as anisotropies, inhomogeneities, etc.
TIME-VARYING LINEAR MODEL APPROXIMATION: APPLICATION TO THERMAL AND AIRFLOW BUILDING SIMULATION
Paris-Sud XI, Université de
TIME-VARYING LINEAR MODEL APPROXIMATION: APPLICATION TO THERMAL AND AIRFLOW BUILDING SIMULATION Nowadays, most of the numerical tools dedicated to simulating the thermal behavior of buildings, consider is demonstrated by its application to the simulation of a multi-zones building. THERMAL AND AIRFLOW MODELS
Distributed state estimation and model predictive control of linear interconnected system
Boyer, Edmond
requirements, modern control systems are becoming more and more complex. For these processes, different controlDistributed state estimation and model predictive control of linear interconnected system: In this paper, a distributed and networked control system architecture based on independent Model Predictive
Plug-and-play decentralized model predictive control for linear systems
Ferrari-Trecate, Giancarlo
1 Plug-and-play decentralized model predictive control for linear systems Stefano Riverso, Graduate to automatize the design of local controllers so that it can be carried out in parallel by smart actuators. In particular, local controllers exploit tube-based Model Predictive Control (MPC) in order to guarantee
Local Genealogies in a Linear Mixed Model for Genome-Wide Association Mapping in Complex
Schierup, Mikkel Heide
Local Genealogies in a Linear Mixed Model for Genome-Wide Association Mapping in Complex Pedigreed fashion. Here, we present a complementary approach, called `GENMIX (genealogy based mixed model)' which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA. Subjects
Non-linear sigma-models and string theories
Sen, A.
1986-10-01T23:59:59.000Z
The connection between sigma-models and string theories is discussed, as well as how the sigma-models can be used as tools to prove various results in string theories. Closed bosonic string theory in the light cone gauge is very briefly introduced. Then, closed bosonic string theory in the presence of massless background fields is discussed. The light cone gauge is used, and it is shown that in order to obtain a Lorentz invariant theory, the string theory in the presence of background fields must be described by a two-dimensional conformally invariant theory. The resulting constraints on the background fields are found to be the equations of motion of the string theory. The analysis is extended to the case of the heterotic string theory and the superstring theory in the presence of the massless background fields. It is then shown how to use these results to obtain nontrivial solutions to the string field equations. Another application of these results is shown, namely to prove that the effective cosmological constant after compactification vanishes as a consequence of the classical equations of motion of the string theory. 34 refs. (LEW)
Efficient Learning and Feature Selection in High Dimensional Regression
Ting, Jo-Anne; D'Souza, Aaron; Vijayakumar, Sethu; Schaal, Stefan
2010-01-01T23:59:59.000Z
We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version...
Sufficient reductions in regressions with elliptically contoured1 inverse predictors2
Bura, Efstathia
for21 the regression of Y on X comprises of a linear and a non-linear component.22 1 Introduction23 There are two general approaches based on inverse regression for estimating the linear sufficient9 reductions with18 parameters (µY , ) and density gY , there is no linear non-trivial sufficient reduction except
(1) Likelihood mode of inference: definitions and results (i) Reference population and model.
Frangakis, Constantine
linear regression model with non additive effect. pr" ` "! CU# ` sr)¤ P u X }|e5 ~ ra 5 ¤ A B pr models. (1) Normal linear regression model with additive effect. pr"a`"! sU# ` sr)¤ P u X }| 5~ rf 5
Mathematical and numerical analysis of a transient non-linear axisymmetric eddy current model
RodrÃguez, Rodolfo
Mathematical and numerical analysis of a transient non-linear axisymmetric eddy current model the theoretically predicted behavior of the method, are reported. Keywords transient eddy current Â· axisymmetric is the accurate computation of power losses in the ferromagnetic components of the core due to hysteresis and eddy-current
NUMERICAL SOLUTION OF A TRANSIENT NON-LINEAR AXISYMMETRIC EDDY CURRENT MODEL WITH NON-LOCAL
RodrÃguez, Rodolfo
NUMERICAL SOLUTION OF A TRANSIENT NON-LINEAR AXISYMMETRIC EDDY CURRENT MODEL WITH NON@ing-mat.udec.cl This paper deals with an axisymmetric transient eddy current problem in conductive nonlinear magnetic media of the proposed scheme. Keywords: transient eddy current problem; electromagnetic losses; nonlinear magnetic
Large Scale Approximate Inference and Experimental Design for Sparse Linear Models
Seeger, Matthias
Large Scale Approximate Inference and Experimental Design for Sparse Linear Models Matthias W.kyb.tuebingen.mpg.de/bs/people/seeger/ 27 June 2008 Matthias W. Seeger (MPI BioCyb) Large Scale Bayesian Experimental Design 27/6/08 1 / 27 Algorithms 4 Magnetic Resonance Imaging Sequences Matthias W. Seeger (MPI BioCyb) Large Scale Bayesian
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
Linear Compositional Delay Model for the Timing Analysis of Sub-Powered Combinational Circuits
Linear Compositional Delay Model for the Timing Analysis of Sub-Powered Combinational Circuits the propagation delay through nanometer CMOS circuits is highly desirable. Statistical Static Timing Analysis to accurately capture the circuit behaviour. In view of this we introduce an Inverse Gaussian Distribution (IGD
Job Scheduling Using successive Linear Programming Approximations of a Sparse Model
Paris-Sud XI, Université de
Job Scheduling Using successive Linear Programming Approximations of a Sparse Model Stephane of parallel jobs on a set of processors either in a cluster or in a multiprocessor computer. For the makespan objective, i.e., the comple- tion time of the last job, this problem has been shown to be NP
Control-relevant Modelling and Linear Analysis of Instabilities in Oxy-fuel Combustion
Foss, Bjarne A.
Control-relevant Modelling and Linear Analysis of Instabilities in Oxy-fuel Combustion Dagfinn combustion have been proposed as an alternative to conventional gas turbine cycles for achieving CO2-capture for CO2 sequestration purposes. While combustion instabilities is a problem in modern conventional gas
Neural Modeling of Non-Linear Processes: Relevance of the Takens-Ma~ne Theorem
Masulli, Francesco
coupled to a 150 MW steam turbine. 1 Introduction The problem of controlling systems characterized by non to be managed (on a typical steam turbine they are about 576,000/hour). Moreover, so far, there are no availableNeural Modeling of Non-Linear Processes: Relevance of the Takens-Ma~n´e Theorem Francesco Masulli
Climate induced changes in benthic macrofauna--A non-linear model approach Karin Junker a,
Dippner, Joachim W.
Climate induced changes in benthic macrofauna--A non-linear model approach Karin Junker a, , Dusan macrofauna communities Climate indices Neural network Climate variability Time series forecasting Regime-nearest neighbours" (OPKNN) are applied to relate various climate indices to time series of biomass, abun- dance
Logistic Model Trees Niels Landwehr 1,2 , Mark Hall 2 , and Eibe Frank 2
Frank, Eibe
Logistic Model Trees Niels Landwehr 1,2 , Mark Hall 2 , and Eibe Frank 2 1 Department of Computer problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way
Poisson loglinear modeling with linear constraints on the expected cell frequencies
Martin, Nirian
2010-01-01T23:59:59.000Z
In this paper we consider Poisson loglinear models with linear constraints (LMLC) on the expected table counts. Multinomial and product multinomial loglinear models can be obtained by considering that some marginal totals (linear constraints on the expected table counts) have been prefixed in a Poisson loglinear model. Therefore with the theory developed in this paper, multinomial and product multinomial loglinear models can be considered as a particular case. To carry out inferences on the parameters in the LMLC an information-theoretic approach is followed from which the classical maximum likelihood estimators and Pearson chi-square statistics for goodness-of fit are obtained. In addition, nested hypotheses are proposed as a general procedure for hypothesis testing. Through a simulation study the appropriateness of proposed inference tools is illustrated.
Wang, Jinrong
1996-01-01T23:59:59.000Z
The measured energy savings from retrofits in commercial buildings are generally determined as the difference between the energy consumption predicted using a baseline model and the measured energy consumption during the post retrofit period. Most...
1Machine Learning, to appear. Least-Squares Independence Regression
Sugiyama, Masashi
1Machine Learning, to appear. Least-Squares Independence Regression for Non-Linear Causal Inference of Technology, Japan. sesejun@cs.titech.ac.jp Abstract The discovery of non-linear causal relationship under Causal inference, Non-Linear, Non-Gaussian, Squared-loss mutual information, Least-Squares Independence
Efficient Locally Weighted Polynomial Regression Predictions Andrew W. Moore
Schneider, Jeff
polynomial regression (LWPR) is a popular instancebased al gorithm for learning continuous nonlinear gorithm for learning continuous nonlinear mappings from realvalued input vectors to realvalued output vectors. It is particularly appropriate for learning com plex highly nonlinear functions of up to about
Non-Linear Drying Diffusion and Viscoelastic Drying Shrinkage Modeling in Hardened Cement Pastes
Leung, Chin K.
2010-07-14T23:59:59.000Z
from creep tests of sealed specimens. 3 2. PREVIOUS WORK 2.1. Non-linear Drying Diffusion Drying mechanisms in porous materials, particularly gels, were extensively studied by Scherer [1]. After an initial period of constant rate of mass loss..., diffusion coefficients were then used as input parameters for the shrinkage model. To verify the poroviscoelastic shrinkage model, creep compliance coefficients of the materials also needed to be obtained from separate creep tests of sealed specimens...
Analytical modeling of a new disc permanent magnet linear synchronous machine for electric vehicles
Liu, C.T.; Chen, J.W.; Su, K.S.
1999-09-01T23:59:59.000Z
This paper develops an analytical approach based on a qd0 reference frame model to analyze dynamic and steady state characteristics of disc permanent magnet linear synchronous machines (DPMLSMs). The established compact mathematical model can be more easily employed to analyze the system behavior and to design the controller. Superiority in operational electromagnetic characteristics of the proposed DPMLSM for electric vehicle (EV) applications is verified by both numerical simulations and experimental investigations.
STAT 563: REGRESSION ANALYSIS 16:960:563:01 SPRING 2012, THURSDAY 6:40-9:30 PM, SEC 203 BUS
Jornsten, Rebecka
STAT 563: REGRESSION ANALYSIS 16:960:563:01 SPRING 2012, THURSDAY 6:40-9:30 PM, SEC 203 BUS 1 or by appointment · Email: hxiao@stat.rutgers.edu · Text: Introduction to Linear Regression Analysis, by Montgomery distribution Supplementary Reading 2 3 Feb 02 Simple linear regression Ch. 2 4 Feb 09 Simple linear regression
Ridge Regression Estimation for Survey Samples Mingue Park
Yang, Min
Ridge Regression Estimation for Survey Samples Mingue Park and Min Yang Korea University and University of Missouri Abstract A procedure for constructing a vector of regression weights is considered. Under the re- gression superpopulation model, the ridge regression estimator that has minimum model mean
An Efficient Algorithm for REML in Heteroscedastic Regression
Smyth, Gordon K.
An Efficient Algorithm for REML in Heteroscedastic Regression Gordon K. Smyth Walter and Eliza Hall of covariates relevant for predicting the variance and and are vectors of regression coefficients. The model). Heteroscedastic regression models have an extensive literature going back to Park (1966), Rutemiller & Bowers
Linear-optical generation of eigenstates of the two-site XY model
Stefanie Barz; Borivoje Dakic; Yannick Ole Lipp; Frank Verstraete; James D. Whitfield; Philip Walther
2014-10-04T23:59:59.000Z
Much of the anticipation accompanying the development of a quantum computer relates to its application to simulating dynamics of another quantum system of interest. Here we study the building blocks for simulating quantum spin systems with linear optics. We experimentally generate the eigenstates of the XY Hamiltonian under an external magnetic field. The implemented quantum circuit consists of two CNOT gates, which are realized experimentally by harnessing entanglement from a photon source and by applying a CPhase gate. We tune the ratio of coupling constants and magnetic field by changing local parameters. This implementation of the XY model using linear quantum optics might open the door to the future studies of quenching dynamics using linear optics.
Multivariate calibration with single-index signal regression Paul H.C. Eilers a
Marx, Brian D.
regression can be extended with an explicit link function between linear prediction and response is being estimated by P-splines. Application to simulations and three data sets shows that if a non-linearity from linear algebra, by non-linear functions. The idea is that a non-linear kernel in the linear space
Ordinary Least Square Regression, Orthogonal Regression, Geometric Mean Regression and their
Ordinary Least Square Regression, Orthogonal Regression, Geometric Mean Regression@notes.cc.sunysb.edu Abstract. Regression analysis, especially the ordinary least squares method which assumes that errors for both measurements. In this work, we examine two regression approaches available to accommodate
Wen-Sheng Xu; Karl F. Freed
2015-06-26T23:59:59.000Z
The lattice cluster theory (LCT) for semiflexible linear telechelic melts, developed in paper I, is applied to examine the influence of chain stiffness on the average degree of self-assembly and the basic thermodynamic properties of linear telechelic polymer melts. Our calculations imply that chain stiffness promotes self-assembly of linear telechelic polymer melts that assemble on cooling when either polymer volume fraction $\\phi$ or temperature $T$ is high, but opposes self-assembly when both $\\phi$ and $T$ are sufficiently low. This allows us to identify a boundary line in the $\\phi$-$T$ plane that separates two regions of qualitatively different influence of chain stiffness on self-assembly. The enthalpy and entropy of self-assembly are usually treated as adjustable parameters in classical Flory-Huggins type theories for the equilibrium self-assembly of polymers, but they are demonstrated here to strongly depend on chain stiffness. Moreover, illustrative calculations for the dependence of the entropy density of linear telechelic polymer melts on chain stiffness demonstrate the importance of including semiflexibility within the LCT when exploring the nature of glass formation in models of linear telechelic polymer melts.
Paris-Sud XI, Université de
/Simulink simulations. Key words: power system harmonics, power electronic, linear time periodic modeling, PWM, control1 POWER ELECTRONICS HARMONIC ANALYSIS BASED ON THE LINEAR TIME PERIODIC MODELING. APPLICATIONS in power electronic systems. The considered system is described by a set of differential equations, which
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
1982-12-01T23:59:59.000Z
Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.
Kernel Regression with Order Preferences Xiaojin Zhu and Andrew B. Goldberg
Zhu, Xiaojin "Jerry"
such knowledge as positive correlation can be difficult in non-linear kernel regression, because of the non-linear, but the exact re- lation is highly non-linear and unknown. We can, however, easily create order preferencesKernel Regression with Order Preferences Xiaojin Zhu and Andrew B. Goldberg Department of Computer
On linear stability and dispersion for crystals in the Schroedinger-Poisson model
Alexander Komech; Elena Kopylova
2015-06-03T23:59:59.000Z
We consider the Schr\\"odinger-Poisson-Newton equations as a model of crystals. Our main results are the well posedness and dispersion decay for the linearized dynamics at the ground state. This linearization is a Hamilton system with nonselfadjoint (and even nonsymmetric) generator. We diagonalize this Hamilton generator using our theory of spectral resolution of the Hamilton operators with positive definite energy which is a special version of the M. Krein - H. Langer theory of selfadjoint operators in the Hilbert spaces with indefinite metric. Using this spectral resolution, we establish the well posedness and the dispersion decay of the linearized dynamics with positive energy. The key result of present paper is the energy positivity for the linearized dynamics with small elementary charge $e>0$ under a novel Wiener-type condition on the ions positions and their charge densitities. We give examples of the crystals satisfying this condition. The main difficulty in the proof ofr the positivity is due to the fact that for $e=0$ the minimal spectral point $E_0=0$ is an eigenvalue of infinite multiplicity for the energy operator. To prove the positivity we study the asymptotics of the ground state as $e\\to 0$ and show that the zero eigenvalue $E_0=0$ bifurcates into $E_e\\sim e^2$.
Thermal history modelling: HeFTy vs. QTQt Pieter Vermeesch , Yuntao Tian
Crawford, Ian
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2.2. Linear regression of weakly non-linear data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2.3. Linear regression of strongly non-linear data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2. Part I: linear regression
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
Conditional Regression Forests for Human Pose Estimation Pushmeet Kohli
Kohli, Pushmeet
Conditional Regression Forests for Human Pose Estimation Min Sun Pushmeet Kohli Jamie Shotton estimation from depth images. The conditional regression model proposed in the paper is general and can body joint prediction as a regression problem which avoids intermediate body part classification
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
Median regression and the missing information principle Ian W. McKeague
McKeague, Ian
Median regression and the missing information principle Ian W. McKeague Department of Statistics regression analysis has robustness properties which make it an attractive alternative to regression based of the median regression model, leading to a new estimator for the regression parameters. Our approach adapts
Baghi, Q; Bergé, J; Christophe, B; Touboul, P; Rodrigues, M
2015-01-01T23:59:59.000Z
The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method which cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive (AR) fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whos...
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12T23:59:59.000Z
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.
Grossmann, Ignacio E.
1 A Mixed-Integer Linear Programming Model for Optimizing the Scheduling and Assignment of Tank, Midland, MI 48674, USA Abstract This paper presents a novel mixed-integer linear programming (MILP multi-product processing lines and the assignment of dedicated storage tanks to finished products
Jia, S.; Chung, B.T.F. [Univ. of Akron, OH (United States). Dept. of Mechanical Engineering
1996-12-31T23:59:59.000Z
Based on a previously proposed non-linear turbulence model, a turbulent heat transfer model is formulated in the present study using the concept of Generalized Gradient Diffusion (GGD) hypothesis. Under this hypothesis, an anisotropic thermal diffusivity can be obtained through the proposed non-linear turbulent model which is applied to the turbulent flow and heat transfer in a sudden expansion pipe with a constant heat flux through the pipe wall. The numerical results are compared with the available experimental data for both turbulent and thermal quantities, with an emphasis on the non-linear heat transfer predictions. The improved results are obtained for the bulk temperature distribution showing that the present non-linear heat transfer model is capable of predicting the anisotropic turbulent heat transfer for the pipe expansion flow. Some limits of the proposed model are also identified and discussed.
Yock, Adam D., E-mail: ADYock@mdanderson.org; Kudchadker, Rajat J. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Rao, Arvind [Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and the Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and the Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Dong, Lei [Scripps Proton Therapy Center, San Diego, California 92121 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Scripps Proton Therapy Center, San Diego, California 92121 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Beadle, Beth M.; Garden, Adam S. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)] [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Court, Laurence E. [Department of Radiation Physics and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Radiation Physics and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)
2014-05-15T23:59:59.000Z
Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: ?11.6%–23.8%) and 14.6% (range: ?7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: ?6.8%–40.3%) and 13.1% (range: ?1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: ?11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.
Angular momentum transport modeling: achievements of a gyrokinetic quasi-linear approach
Cottier, P; Camenen, Y; Gurcan, O D; Casson, F J; Garbet, X; Hennequin, P; Tala, T
2014-01-01T23:59:59.000Z
QuaLiKiz, a model based on a local gyrokinetic eigenvalue solver is expanded to include momentum flux modeling in addition to heat and particle fluxes. Essential for accurate momentum flux predictions, the parallel asymmetrization of the eigenfunctions is successfully recovered by an analytical fluid model. This is tested against self-consistent gyrokinetic calculations and allows for a correct prediction of the ExB shear impact on the saturated potential amplitude by means of a mixing length rule. Hence, the effect of the ExB shear is recovered on all the transport channels including the induced residual stress. Including these additions, QuaLiKiz remains ~10 000 faster than non-linear gyrokinetic codes allowing for comparisons with experiments without resorting to high performance computing. The example is given of momentum pinch calculations in NBI modulation experiments.
The fixed structurally robust internal model principle for linear multivariable regulators
McGrath, John Thomas
1980-01-01T23:59:59.000Z
for the degree of I'V. STER OF S"IENCE Vay 1980 Va jor Sub jec ~: Elec+r ical Engineering THE FIXED STRUCTURALLY ROBUST INTERNAL MODEL PRINCIPLE FOR LINEAR MULTIVARIABLE REGUIATORS A Thesis by JOHN THOMAS MCGRATH Aoproved as to style and content by... Multivariable Regulators. (May 19BC) John Thomas McGrath, B. S. , Texas ARM Unive sity Chairman of Advisory Committee: Dr. Ralph Keary Cavin III In this paper we develop the necessary and suffi- cient cond'tions to establish the new concept of' a fixed...
M. Abu-Shady
2014-03-13T23:59:59.000Z
A baryonic chemical potential is included in the linear sigma model at finite temperature. The effective mesonic potential is numerically calculated using the midpoint technique. The meson masses are investigated as functions of the temperature at fixed value of baryonic chemical potential. The pressure and energy density are investigated as functions of temperature at fixed value of chemical potential. The obtained results are in good agreement in comparison with other techniques. We conclude that the calculated effective potential successfully predicts the meson properties and thermodynamic properties at finite baryonic chemical potential.
Effect of the scalar condensate on the linear gauge field response in the Abelian Higgs model
Jakovác, A; Szép, Z; Szep, Zs.
2001-01-01T23:59:59.000Z
The effective equations of motion for low-frequency mean gauge fields in the Abelian Higgs model are investigated in the presence of a scalar condensate, near the high temperature equilibrium. We determine the current induced by an inhomogeneous background gauge field in the linear response approximation up to order $e^4$, assuming adiabatic variation of the scalar fields. The physical degrees of freedom are found and a physical gauge choice for the numerical study of the combined Higgs+gauge evolution is proposed.
The 2-dimensional non-linear sigma-model on a random latice
B. Alles; M. Beccaria
1995-03-28T23:59:59.000Z
The O(n) non-linear $\\sigma$-model is simulated on 2-dimensional regular and random lattices. We use two different levels of randomness in the construction of the random lattices and give a detailed explanation of the geometry of such lattices. In the simulations, we calculate the mass gap for $n=3, 4$ and 8, analysing the asymptotic scaling of the data and computing the ratio of Lambda parameters $\\Lambda_{\\rm random}/\\Lambda_{\\rm regular}$. These ratios are in agreement with previous semi-analytical calculations. We also numerically calculate the topological susceptibility by using the cooling method.
Bo Yang; Xihua Xu; John Z. F. Pang; Christopher Monterola
2015-04-06T23:59:59.000Z
We propose a framework for constructing microscopic traffic models from microscopic acceleration patterns that can in principle be experimental measured and proper averaged. The exact model thus obtained can be used to justify the consistency of various popular models in the literature. Assuming analyticity of the exact model, we suggest that a controlled expansion around the constant velocity, uniform headway "ground state" is the proper way of constructing various different effective models. Assuming a unique ground state for any fixed average density, we discuss the universal properties of the resulting effective model, focusing on the emergent quantities of the coupled non-linear ODEs. These include the maximum and minimum headway that give the coexistence curve in the phase diagram, as well as an emergent intrinsic scale that characterizes the strength of interaction between clusters, leading to non-trivial cluster statistics when the unstable ground state is randomly perturbed. Utilizing the universal properties of the emergent quantities, a simple algorithm for constructing an effective traffic model is also presented. The algorithm tunes the model with statistically well-defined quantities extracted from the flow-density plot, and the resulting effective model naturally captures and predicts many quantitative and qualitative empirical features of the highway traffic, especially in the presence of an on-ramp bottleneck. The simplicity of the effective model provides strong evidence that stochasticity, diversity of vehicle types and modeling of complicated individual driving behaviors are \\emph{not} fundamental to many observations of the complex spatiotemporal patterns in the real traffic dynamics. We also propose the nature of the congested phase can be well characterized by the long lasting transient states of the effective model, from which the wide moving jams evolve.
Eck, H. J. N. van; Koppers, W. R.; Rooij, G. J. van; Goedheer, W. J.; Cardozo, N. J. Lopes; Kleyn, A. W. [FOM-Institute for Plasma Physics Rijnhuizen, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, 3430 BE Nieuwegein (Netherlands); Engeln, R.; Schram, D. C. [Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands)
2009-03-15T23:59:59.000Z
The direct simulation Monte Carlo (DSMC) method was used to investigate the efficiency of differential pumping in linear plasma generators operating at high gas flows. Skimmers are used to separate the neutrals from the plasma beam, which is guided from the source to the target by a strong axial magnetic field. In this way, the neutrals are prevented to reach the target region. The neutral flux to the target must be lower than the plasma flux to enable ITER relevant plasma-surface interaction (PSI) studies. It is therefore essential to control the neutral gas dynamics. The DSMC method was used to model the expansion of a hot gas in a low pressure vessel where a small discrepancy in shock position was found between the simulations and a well-established empirical formula. Two stage differential pumping was modeled and applied in the linear plasma devices Pilot-PSI and PLEXIS. In Pilot-PSI a factor of 4.5 pressure reduction for H{sub 2} has been demonstrated. Both simulations and experiments showed that the optimum skimmer position depends on the position of the shock and therefore shifts for different gas parameters. The shape of the skimmer has to be designed such that it has a minimum impact on the shock structure. A too large angle between the skimmer and the forward direction of the gas flow leads to an influence on the expansion structure. A pressure increase in front of the skimmer is formed and the flow of the plasma beam becomes obstructed. It has been shown that a skimmer with an angle around 53 deg. gives the best performance. The use of skimmers is implemented in the design of the large linear plasma generator Magnum-PSI. Here, a three stage differentially pumped vacuum system is used to reach low enough neutral pressures near the target, opening a door to PSI research in the ITER relevant regime.
Fatemi, Ali
Application of bi-linear loglog SN model to strain-controlled fatigue data of aluminum alloyslog model is applied to stress amplitude versus fatigue life data of 14 aluminum alloys. It is shown-life curves are discussed. Life predictions of aluminum alloys based on linear and bi-linear models are also
LOG HAZARD REGRESSION Huiying Sun
Heckman, Nancy E.
LOG HAZARD REGRESSION by Huiying Sun Ph.D, Harbin Institute of Technology, Harbin, CHINA, 1991 regression splines to estimate the two log marginal hazard func tions of bivariate survival times, where, 1995) hazard regression for estimating a univariate survival time. We derive an approach to find
CORRESPONDENCE Reduced major axis regression
CORRESPONDENCE Reduced major axis regression and the island rule The Ôisland ruleÕ describes). The slope of the least-squares regression of Y on X, b, measures the extent to which large body sizes tend- ferent way, as the regression of Y ) X on X, and here the slope (b¢) is generally negative (b¢ = b ) 1
Experimental characterization and modeling of non-linear coupling of the LHCD power on Tore Supra
Preynas, M. [Max Planck Institut für Plasmaphysik, EURATOM Association, D-17491 Greifswald (Germany); Goniche, M.; Hillairet, J.; Litaudon, X.; Ekedahl, A. [CEA, IRFM, F-13108 Saint Paul lez Durance (France)
2014-02-12T23:59:59.000Z
To achieve steady state operation on future tokamaks, in particular on ITER, the unique capability of a LHCD system to efficiently drive off-axis non-inductive current is needed. In this context, it is of prime importance to study and master the coupling of LH wave to the core plasma at high power density (tens of MW/m{sup 2}). In some specific conditions, deleterious effects on the LHCD coupling are sometimes observed on Tore Supra. At high power the waves may modify the edge parameters that change the wave coupling properties in a non-linear manner. In this way, dedicated LHCD experiments have been performed using the LHCD system of Tore Supra, composed of two different conceptual designs of launcher: the Fully Active Multijunction (FAM) and the new Passive Active Multijunction (PAM) antennas. A nonlinear interaction between the electron density and the electric field has been characterized in a thin plasma layer in front of the two LHCD antennas. The resulting dependence of the power reflection coefficient with the LHCD power, leading occasionally to trips in the output power, is not predicted by the standard linear theory of the LH wave coupling. Therefore, it is important to investigate and understand the possible origin of such non-linear effects in order to avoid their possible deleterious consequences. The PICCOLO-2D code, which self-consistently treats the wave propagation in the antenna vicinity and its interaction with the local edge plasma density, is used to simulate Tore Supra discharges. The simulation reproduces very well the occurrence of a non-linear behavior in the coupling observed in the LHCD experiments. The important differences and trends between the FAM and the PAM antennas, especially a larger increase in RC for the FAM, are also reproduced by the PICCOLO-2D simulation. The working hypothesis of the contribution of the ponderomotive effect in the non-linear observations of LHCD coupling is therefore validated through this comprehensive modeling for the first time on the FAM and PAM antennas on Tore Supra.
REGRESSION PERFORMANCE OF GROUP LASSO FOR ARBITRARY DESIGN MATRICES Marco F. Duarte,1,
Bajwa, Waheed U.
REGRESSION PERFORMANCE OF GROUP LASSO FOR ARBITRARY DESIGN MATRICES Marco F. Duarte,1, Waheed U.duarte,w.bajwa,robert.calderbank}@duke.edu ABSTRACT In many linear regression problems, explanatory variables are activated in groups or clusters; group lasso has been proposed for regression in such cases. This paper studies the non- asymptotic
Modelling and Linear Control of a Buoyancy-Driven Airship Xiaotao WU Claude H. MOOG and Yueming HU
Paris-Sud XI, Université de
Modelling and Linear Control of a Buoyancy-Driven Airship Xiaotao WU Claude H. MOOG and Yueming HU Abstract-- We describe the modelling and control of a new- kind airship which is propelled by buoyancy gliders and aircraft, a 6DOF nonlinear mathematical model of a buoyancy-driven airship is derived
Brunner, S. [Centre de Recherches en Physique des Plasmas, Association Euratom-Confédération Suisse, Ecole Polytechnique Fédérale de Lausanne, Lausanne, (Switzerland); Berger, R. L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Cohen, B. I. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hausammann, L. [Centre de Recherches en Physique des Plasmas, Association Euratom-Confédération Suisse, Ecole Polytechnique Fédérale de Lausanne, Lausanne, (Switzerland); Valeo, E. J. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
2014-10-01T23:59:59.000Z
Kinetic Vlasov simulations of one-dimensional finite amplitude Electron Plasma Waves are performed in a multi-wavelength long system. A systematic study of the most unstable linear sideband mode, in particular its growth rate ? and quasi- wavenumber ?k, is carried out by scanning the amplitude and wavenumber of the initial wave. Simulation results are successfully compared against numerical and analytical solutions to the reduced model by Kruer et al. [Phys. Rev. Lett. 23, 838 (1969)] for the Trapped Particle Instability (TPI). A model recently suggested by Dodin et al. [Phys. Rev. Lett. 110, 215006 (2013)], which in addition to the TPI accounts for the so-called Negative Mass Instability because of a more detailed representation of the trapped particle dynamics, is also studied and compared with simulations.
Brunner, S., E-mail: stephan.brunner@epfl.ch; Hausammann, L. [Centre de Recherches en Physique des Plasmas, Association Euratom-Confédération Suisse, Ecole Polytechnique Fédérale de Lausanne, CRPP-PPB, CH-1015 Lausanne (Switzerland); Berger, R. L., E-mail: berger5@llnl.gov; Cohen, B. I. [Lawrence Livermore National Laboratory, University of California, P.O. Box 808, Livermore, California 94551 (United States); Valeo, E. J. [Princeton Plasma Physics Laboratory, P.O. Box 451, Princeton, New Jersey 08543-0451 (United States)
2014-10-15T23:59:59.000Z
Kinetic Vlasov simulations of one-dimensional finite amplitude Electron Plasma Waves are performed in a multi-wavelength long system. A systematic study of the most unstable linear sideband mode, in particular its growth rate ? and quasi- wavenumber ?k, is carried out by scanning the amplitude and wavenumber of the initial wave. Simulation results are successfully compared against numerical and analytical solutions to the reduced model by Kruer et al. [Phys. Rev. Lett. 23, 838 (1969)] for the Trapped Particle Instability (TPI). A model recently suggested by Dodin et al. [Phys. Rev. Lett. 110, 215006 (2013)], which in addition to the TPI accounts for the so-called Negative Mass Instability because of a more detailed representation of the trapped particle dynamics, is also studied and compared with simulations.
A semiempirical linear model of indirect, flat-panel x-ray detectors
Huang, Shih-Ying; Yang Kai; Abbey, Craig K.; Boone, John M. [Department of Biomedical Engineering, University of California, Davis, California, One Shields Avenue, Davis, California 95616 (United States) and Department of Radiology, University of California, Davis, Medical Center, 4860 Y Street, Ambulatory Care Center Suite 0505, Sacramento, California 95817 (United States); Department of Radiology, University of California, Davis, Medical Center, 4860 Y Street, Ambulatory Care Center Suite 0505, Sacramento, California 95817 (United States); Department of Psychological and Brain Sciences, University of California, Santa Barbara, California 92106 (United States); Department of Biomedical Engineering, University of California, Davis, California, One Shields Avenue, Davis, California 95616 (United States) and Department of Radiology, University of California, Davis, Medical Center, 4860 Y Street, Ambulatory Care Center Suite 3100, Sacramento, California 95817 (United States)
2012-04-15T23:59:59.000Z
Purpose: It is important to understand signal and noise transfer in the indirect, flat-panel x-ray detector when developing and optimizing imaging systems. For optimization where simulating images is necessary, this study introduces a semiempirical model to simulate projection images with user-defined x-ray fluence interaction. Methods: The signal and noise transfer in the indirect, flat-panel x-ray detectors is characterized by statistics consistent with energy-integration of x-ray photons. For an incident x-ray spectrum, x-ray photons are attenuated and absorbed in the x-ray scintillator to produce light photons, which are coupled to photodiodes for signal readout. The signal mean and variance are linearly related to the energy-integrated x-ray spectrum by empirically determined factors. With the known first- and second-order statistics, images can be simulated by incorporating multipixel signal statistics and the modulation transfer function of the imaging system. To estimate the semiempirical input to this model, 500 projection images (using an indirect, flat-panel x-ray detector in the breast CT system) were acquired with 50-100 kilovolt (kV) x-ray spectra filtered with 0.1-mm tin (Sn), 0.2-mm copper (Cu), 1.5-mm aluminum (Al), or 0.05-mm silver (Ag). The signal mean and variance of each detector element and the noise power spectra (NPS) were calculated and incorporated into this model for accuracy. Additionally, the modulation transfer function of the detector system was physically measured and incorporated in the image simulation steps. For validation purposes, simulated and measured projection images of air scans were compared using 40 kV/0.1-mm Sn, 65 kV/0.2-mm Cu, 85 kV/1.5-mm Al, and 95 kV/0.05-mm Ag. Results: The linear relationship between the measured signal statistics and the energy-integrated x-ray spectrum was confirmed and incorporated into the model. The signal mean and variance factors were linearly related to kV for each filter material (r{sup 2} of signal mean to kV: 0.91, 0.93, 0.86, and 0.99 for 0.1-mm Sn, 0.2-mm Cu, 1.5-mm Al, and 0.05-mm Ag, respectively; r{sup 2} of signal variance to kV: 0.99 for all four filters). The comparison of the signal and noise (mean, variance, and NPS) between the simulated and measured air scan images suggested that this model was reasonable in predicting accurate signal statistics of air scan images using absolute percent error. Overall, the model was found to be accurate in estimating signal statistics and spatial correlation between the detector elements of the images acquired with indirect, flat-panel x-ray detectors. Conclusions: The semiempirical linear model of the indirect, flat-panel x-ray detectors was described and validated with images of air scans. The model was found to be a useful tool in understanding the signal and noise transfer within indirect, flat-panel x-ray detector systems.
Efficient modelling of particle collisions using a non-linear viscoelastic contact force
Ray, Shouryya; Fröhlich, Jochen
2015-01-01T23:59:59.000Z
In this paper the normal collision of spherical particles is investigated. The particle interaction is modelled in a macroscopic way using the Hertzian contact force with additional linear damping. The goal of the work is to develop an efficient approximate solution of sufficient accuracy for this problem which can be used in soft-sphere collision models for Discrete Element Methods and for particle transport in viscous fluids. First, by the choice of appropriate units, the number of governing parameters of the collision process is reduced to one, thus providing a dimensionless parameter that characterizes all such collisions up to dynamic similitude. It is a simple combination of known material parameters as well as initial conditions. A rigorous calculation of the collision time and restitution coefficient from the governing equations, in the form of a series expansion in this parameter is provided. Such a first principles calculation is particularly interesting from a theoretical perspective. Since the gov...
Non-Linear Poisson-Boltzmann Theory of a Wigner-Seitz Model for Swollen Clays
R. J. F. Leote de Carvalho; E. Trizac; J. -P. Hansen
1999-12-06T23:59:59.000Z
Swollen stacks of finite-size disc-like Laponite clay platelets are investigated within a Wigner-Seitz cell model. Each cell is a cylinder containing a coaxial platelet at its centre, together with an overall charge-neutral distribution of microscopic co and counterions, within a primitive model description. The non-linear Poisson-Boltzmann (PB) equation for the electrostatic potential profile is solved numerically within a highly efficient Green's function formulation. Previous predictions of linearised Poisson-Boltzmann (LPB) theory are confirmed at a qualitative level, but large quantitative differences between PB and LPB theories are found at physically relevant values of the charge carried by the platelets. A hybrid theory treating edge effect at the linearised level yields good potential profiles. The force between two coaxial platelets, calculated within PB theory, is an order of magnitude smaller than predicted by LPB theory
Neutral Higgs boson pair production at the linear collider in the noncommutative standard model
Das, Prasanta Kumar; Prakash, Abhishodh; Mitra, Anupam [Birla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus, NH-17B, Zuarinagar, Goa-403726 (India)
2011-03-01T23:59:59.000Z
We study the Higgs boson pair production at the linear collider in the noncommutative extension of the standard model using the Seiberg-Witten map of this to the first order of the noncommutative parameter {Theta}{sub {mu}{nu}}. Unlike the standard model (where the process is forbidden) here the Higgs boson pair directly interacts with the photon. We find that the pair production cross section can be quite significant for the noncommutative scale {Lambda} lying in the range 0.5 TeV to 1.0 TeV. Using the experimental (LEP 2, Tevatron, and global electroweak fit) bound on the Higgs mass, we obtain 626 GeV{<=}{Lambda}{<=}974 GeV.
Kim, Ji Myong
2013-07-31T23:59:59.000Z
Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant...
Kim, Ji Myong
2013-07-31T23:59:59.000Z
be diverse. For instance, the Federal Emergency Management Agency (FEMA) created the FEMA Q3 Flood Data study in an effort to understand the risks of hurricanes and floods. FEMA designated flood zones based on the level of flood risk (Fulton County 2012...). The zones show the potential risk of flood in each defined area. As shown in Table 1, there are three types of flood zones. Zone A is an area anticipated to have a 1%, or larger chance to flood in any given year. Zone X500 is an area anticipated to have a...
On Discriminative Joint Density Modeling Jarkko Salojarvi1
Kaski, Samuel
missing values, since the model is #12;assumed to generate also the covariates x. The models are often- criminative cost function, the conditional likelihood. We use the frame- work to derive generative models for generalized linear models, including logistic regression, linear discriminant analysis, and discriminative mix
Lagrangian perturbations and the matter bispectrum I: fourth-order model for non-linear clustering
Rampf, Cornelius [Institut für Theoretische Teilchenphysik und Kosmologie, RWTH Aachen, Physikzentrum RWTH-Melaten, D-52056 Aachen (Germany); Buchert, Thomas, E-mail: rampf@physik.rwth-aachen.de, E-mail: buchert@obs.univ-lyon1.fr [Université de Lyon, Observatoire de Lyon, Centre de Recherche Astrophysique de Lyon, CNRS UMR 5574: Université Lyon 1 and École Normale Supérieure de Lyon, 9 avenue Charles André, F-69230 Saint-Genis-Laval (France)
2012-06-01T23:59:59.000Z
We investigate the Lagrangian perturbation theory of a homogeneous and isotropic universe in the non-relativistic limit, and derive the solutions up to the fourth order. These solutions are needed for example for the next-to-leading order correction of the (resummed) Lagrangian matter bispectrum, which we study in an accompanying paper. We focus on flat cosmologies with a vanishing cosmological constant, and provide an in-depth description of two complementary approaches used in the current literature. Both approaches are solved with two different sets of initial conditions — both appropriate for modelling the large-scale structure. Afterwards we consider only the fastest growing mode solution, which is not affected by either of these choices of initial conditions. Under the reasonable approximation that the linear density contrast is evaluated at the initial Lagrangian position of the fluid particle, we obtain the nth-order displacement field in the so-called initial position limit: the nth order displacement field consists of 3(n-1) integrals over n linear density contrasts, and obeys self-similarity. Then, we find exact relations between the series in Lagrangian and Eulerian perturbation theory, leading to identical predictions for the density contrast and the peculiar-velocity divergence up to the fourth order.
On linear stability and dispersion for crystals in the Schroedinger-Poisson model
Alexander Komech; Elena Kopylova
2015-08-22T23:59:59.000Z
We consider the Schr\\"odinger-Poisson-Newton equations as a model of crystals. Our basic results are the stability and the dispersion decay for the linearized dynamics at the ground state for crystals with a cubic lattice and one ion per cell. This linearization is a Hamilton system with nonselfadjoint (and even nonsymmetric) generator. We diagonalize this Hamilton generator in the Bloch representation using our theory of spectral resolution of the Hamilton ope\\-rators with positive definite energy \\ci{KK2014a,KK2014b}. Using this spectral resolution, we establish the stability the dispersion decay. Our key result is the energy positivity of the Bloch generators for small elementary charge $e>0$ under a novel Wiener-type condition on the ion charge density. The corresponding examples are given. To prove the positivity we construct the asymptotics of the ground state as $e\\to 0$ and show that the minimal zero eigenvalue, corresponding to $e=0$, bifurcates into positive eigenvalues $\\sim e^2$.
The curse of dimension in nonparametric regression
Kpotufe, Samory
2010-01-01T23:59:59.000Z
3.2.1 Kernel regression . . . . . . . . . . . . . . . . . .3.2.2 k-NN regression . . . . . . . . . . . . . . . . . .1.1 Nonparametric regression . . . . . . . . . . . 1.1.1
Complete regression of melanoma associated with vitiligo
Piqué-Duran, Enric; Palacios-Llopis, Santiago; Martínez-Martín, MªSol; Pérez-Cejudo, Juan A
2011-01-01T23:59:59.000Z
melanoma: influence of regression. Histopathology 1983; 7:Varga E, Dobozy A. Tumor regression predicts higher risk ofcutaneous melanoma with regression doesnot require a lower
Fejos, G
2015-01-01T23:59:59.000Z
Temperature dependence of the $U_A(1)$ anomaly is investigated by taking into account mesonic fluctuations in the $U(3)\\times U(3)$ linear sigma model. A field dependent anomaly coefficient function of the effective potential is calculated within the finite temperature functional renormalization group approach. The applied approximation scheme is a generalization of the chiral invariant expansion technique developed in [G. Fej\\H{o}s, Phys. Rev. D 90, 096011 (2014)]. We provide an analytic expression and also numerical evidence that depending on the relationship between the two quartic couplings, mesonic fluctuations can either strengthen of weaken the anomaly as a function of the temperature. Role of the six-point invariant of the $U(3)\\times U(3)$ group, and therefore the stability of the chiral expansion is also discussed in detail.
G. Fejos
2015-06-29T23:59:59.000Z
Temperature dependence of the $U_A(1)$ anomaly is investigated by taking into account mesonic fluctuations in the $U(3)\\times U(3)$ linear sigma model. A field dependent anomaly coefficient function of the effective potential is calculated within the finite temperature functional renormalization group approach. The applied approximation scheme is a generalization of the chiral invariant expansion technique developed in [G. Fejos, Phys. Rev. D 90, 096011 (2014)]. We provide an analytic expression and also numerical evidence that depending on the relationship between the two quartic couplings, mesonic fluctuations can either strengthen of weaken the anomaly as a function of the temperature. Role of the six-point invariant of the $U(3)\\times U(3)$ group, and therefore the stability of the chiral expansion is also discussed in detail.
Reynolds, Jacob G. [Washington River Protection Solutions, Richland, WA (United States)
2013-01-11T23:59:59.000Z
Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a change in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH{sub 4}H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results detennined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.
OFS model-based adaptive control for block-oriented non-linear Systems
Cambridge, University of
-type non-linear systems (Go´mez and Baeyens, 2004; Henson, 1997). Wiener-type systems consist of a linear the same elements in reverse order (Go´mez and Baeyens, 2004). In recent years, the control of these types of systems has become one of the most important and difficult tasks in non-linear control field (Go´mez
Fernandez, Thomas
regression [5], [6] that evolves linear combinations of non-linear transformations of the input Manuscript non-linear transformations of the input variables. The functionality of GPTIPS is demonstrated regression by genetic programming (GP) is introduced. GPTIPS is specifically designed to evolve mathematical
N=(4,4) Gauged Linear Sigma Models for Defect Five-branes
Tetsuji Kimura
2015-06-18T23:59:59.000Z
We study two-dimensional ${\\cal N}=(4,4)$ gauged linear sigma model (GLSM). Its low energy effective theory is a nonlinear sigma model whose target space gives rise to a configuration of five-branes in string theory. In this article we focus on sigma models for NS5-branes, KK5-branes and an exotic $5^2_2$-brane. In particular, we carefully analyze the GLSM for an exotic $5^2_2$-brane whose background configuration is multi-valued. The exotic $5^2_2$-brane is a concrete example of nongeometric configuration in string theory. We find that the exotic feature originates from the string winding coordinate in a very clear way. In order to complete this analysis, we propose a duality transformation formula which converts an ${\\cal N}=(2,2)$ chiral superfield in F-term to a twisted chiral superfield coupled to an unconstrained complex superfield. This article is a short review based on arXiv:1304.4061 in collaboration with Shin Sasaki.
N=(4,4) Gauged Linear Sigma Models for Defect Five-branes
Kimura, Tetsuji
2015-01-01T23:59:59.000Z
We study two-dimensional ${\\cal N}=(4,4)$ gauged linear sigma model (GLSM). Its low energy effective theory is a nonlinear sigma model whose target space gives rise to a configuration of five-branes in string theory. In this article we focus on sigma models for NS5-branes, KK5-branes and an exotic $5^2_2$-brane. In particular, we carefully analyze the GLSM for an exotic $5^2_2$-brane whose background configuration is multi-valued. The exotic $5^2_2$-brane is a concrete example of nongeometric configuration in string theory. We find that the exotic feature originates from the string winding coordinate in a very clear way. In order to complete this analysis, we propose a duality transformation formula which converts an ${\\cal N}=(2,2)$ chiral superfield in F-term to a twisted chiral superfield coupled to an unconstrained complex superfield. This article is a short review based on arXiv:1304.4061 in collaboration with Shin Sasaki.
Linear and Nonlinear Modeling of a Traveling-Wave Thermoacoustic Heat Engine
Scalo, Carlo; Hesselink, Lambertus
2014-01-01T23:59:59.000Z
We have carried out three-dimensional Navier-Stokes simulations, from quiescent conditions to the limit cycle, of a traveling-wave thermoacoustic heat engine (TAE) composed of a long variable-area resonator shrouding a smaller annular tube, which encloses the hot (HHX) and ambient (AHX) heat-exchangers, and the regenerator (REG). Simulations are wall-resolved, with no-slip and adiabatic conditions enforced at all boundaries, while the heat transfer and drag due to the REG and HXs are modeled. HHX temperatures have been investigated in the range 440K - 500K with AHX temperature fixed at 300K. The initial exponential growth of acoustic energy is due to a network of traveling waves amplified by looping around the REG/HX unit in the direction of the imposed temperature gradient. A simple analytical model demonstrates that such thermoacoustic instability is a Lagrangian thermodynamic process resembling a Stirling cycle. A system-wide linear stability model based on Rott's theory is able to accurately predict the f...
W.R. Tobler Bidimensional Regression
Tobler, Waldo
W.R. Tobler Bidimensional Regression Since its invention by Francis Galton in 1877 regression. Here the regression coefficients constitute a spatially varying, but coordinate invariant, second projection. In a computer implementation a nonparametric approach allows visualization of the regression
A note on wavelet estimation of the derivatives of a regression function in a
Paris-Sud XI, Université de
A note on wavelet estimation of the derivatives of a regression function in a random design setting of the derivatives of a regression function in the nonparametric regression model with random design. New wavelet. Keywords and phrases: Nonparametric regression, Derivatives function estimation, Wavelets, Besov balls
Limited Dependent Variable Correlated Random Coefficient Panel Data Models
Liang, Zhongwen
2012-10-19T23:59:59.000Z
for the average slopes of a linear CRC model with a general nonparametric correlation between regressors and random coefficients. I construct a sqrt(n) consistent estimator for the average slopes via varying coefficient regression. The identification of binary...
Study of Higgs self couplings of a supersymmetric $E_6$ model at the International Linear Collider
S. W. Ham; Kideok Han; Jungil Lee; S. K. Oh
2009-11-30T23:59:59.000Z
We study the Higgs self couplings of a supersymmetric $E_6$ model that has two Higgs doublets and two Higgs singlets. The lightest scalar Higgs boson in the model may be heavier than 112 GeV, at the one-loop level, where the negative results for the Higgs search at the LEP2 experiments are taken into account. The contributions from the top and scalar top quark loops are included in the radiative corrections to the one-loop mass of the lightest scalar Higgs boson, in the effective potential approximation. The effect of the Higgs self couplings may be observed in the production of the lightest scalar Higgs bosons in $e^+e^-$ collisions at the International Linear Collider (ILC) via double Higgs-strahlung process. For the center of mass energy of 500 GeV with the integrated luminosity of 500 fb$^{-1}$ and the efficiency of 20 %, we expect that at least 5 events of the lightest scalar Higgs boson may be produced at the ILC via double Higgs-strahlung process.
Bulk viscosity and the phase transition of the linear sigma model
Antonio Dobado; Juan M. Torres-Rincon
2012-10-04T23:59:59.000Z
In this work we deal with the critical behavior of the bulk viscosity in the linear sigma model (LSM) as an example of a system which can be treated by using different techniques. Starting from the Boltzmann-Uehling-Uhlenbeck equation we compute the bulk viscosity over entropy density of the LSM in the large-N limit. We search for a possible maximum of the bulk viscosity over entropy density at the critical temperature of the chiral phase transition. The information about this critical temperature, as well as the effective masses, is obtained from the effective potential. We find that the expected maximum (as a measure of the conformality loss) is absent in the large N in agreement with other models in the same limit. However, this maximum appears when, instead of the large-N limit, the Hartree approximation within the Cornwall-Jackiw-Tomboulis (CJT) formalism is used. Nevertheless, this last approach to the LSM does not give rise to the Goldstone theorem and also predicts a first order phase transition instead of the expected second order one. Therefore both, the large-N limit and the CJT-Hartree approximations, should be considered as complementary for the study of the critical behavior of the bulk viscosity in the LSM.
The two-phase issue in the O(n) non-linear $?$-model: A Monte Carlo study
B. Alles; A. Buonanno; G. Cella
1996-08-01T23:59:59.000Z
We have performed a high statistics Monte Carlo simulation to investigate whether the two-dimensional O(n) non-linear sigma models are asymptotically free or they show a Kosterlitz- Thouless-like phase transition. We have calculated the mass gap and the magnetic susceptibility in the O(8) model with standard action and the O(3) model with Symanzik action. Our results for O(8) support the asymptotic freedom scenario.
Kernel Regression in the Presence of Correlated Errors Kernel Regression in the Presence in nonparametric regression is difficult in the presence of correlated errors. There exist a wide variety vector machines for regression. Keywords: nonparametric regression, correlated errors, bandwidth choice
Measurement and Modeling of Solute Diffusion Coefficients in Unsaturated Soils
Chou, Hsin-Yi
2010-01-01T23:59:59.000Z
data and the non-linear regression fitted lines of Olsen anddata and the non-linear regression fitted lines of powerdata and the non-linear regression fitted lines of the two-
Efficient modelling of particle collisions using a non-linear viscoelastic contact force
Shouryya Ray; Tobias Kempe; Jochen Fröhlich
2015-06-21T23:59:59.000Z
In this paper the normal collision of spherical particles is investigated. The particle interaction is modelled in a macroscopic way using the Hertzian contact force with additional linear damping. The goal of the work is to develop an efficient approximate solution of sufficient accuracy for this problem which can be used in soft-sphere collision models for Discrete Element Methods and for particle transport in viscous fluids. First, by the choice of appropriate units, the number of governing parameters of the collision process is reduced to one, which is a simple combination of known material parameters as well as initial conditions. It provides a dimensionless parameter that characterizes all such collisions up to dynamic similitude. Next, a rigorous calculation of the collision time and restitution coefficient from the governing equations, in the form of a series expansion in this parameter is provided. Such a calculation based on first principles is particularly interesting from a theoretical perspective. Since the governing equations present some technical difficulties, the methods employed are also of interest from the point of view of the analytical technique. Using further approximations, compact expressions for the restitution coefficient and the collision time are then provided. These are used to implement an approximate algebraic rule for computing the desired stiffness and damping in the framework of the adaptive collision model (Kempe & Fr\\"{o}hlich, J. Fluid Mech., 709: 445-489, 2012). Numerical tests with binary as well as multiple particle collisions are reported to illustrate the accuracy of the proposed method and its superiority in terms of numerical efficiency.
Istrail, Sorin
Lattice and Off-Lattice Side Chain Models of Protein Folding: Linear Time Structure Prediction This paper considers the protein structure prediction problem for lattice and off-lattice protein folding tools for reasoning about protein folding in unrestricted continuous space through anal- ogy. This paper
Borchers, Brian
Linear and Nonlinear Models for Inversion of Electrical Conductivity Pro les in Field Soils from EM to thank Dr. Jan Hendricks of the New Mexico Tech Hydrology department for allowing me to research in soil by Khe-Sing The. ii #12;ABSTRACT The EM-38 is an instrument used to measure conductivity in the soil
A O(n^8) X O(n^7) Linear Programming Model of the Quadratic Assignment Problem
Diaby, Moustapha
2008-01-01T23:59:59.000Z
In this paper, we propose a linear programming (LP) formulation of the Quadratic Assignment Problem (QAP) with O(n^8) variables and O(n^7) constraints, where n is the number of assignments. A small experimentation that was undertaken in order to gain some rough indications about the computational performance of the model is discussed.
J., Selvaganapathy; Konar, Partha
2015-01-01T23:59:59.000Z
We study the associated Higgs production with Z boson at future linear colliders in the framework of the minimal noncommutative standard model. Using the Seiberg-Witten map, we calculate the production cross-section considering all orders of the noncommutative parameter $\\Theta_{\\mu\
Single-Hop Case Typically, linear regression (using the last
temperature, voltage, etc... (30-100 ppm) Christoph Lenzen, Philipp Sommer, Roger Wattenhofer Computer of the 2nd International Conference on Embedded Networked Sensor Systems, 2004. [Sommer09] P. Sommer and R International Conference on Information Processing in Sensor Networks, 2009. [Lenzen09] C. Lenzen, P. Sommer
Empirical modeling of end-to-end delay dynamics in best-effort networks
Doddi, Srikar
2005-08-29T23:59:59.000Z
identification models Auto-Regressive eXogenous (AR) and Auto-Regressive Moving Average with eXtra / eXternal (ARMA) and non-linear models like the Feedforwad Multi-layer Perceptron (FMLP) have been found to perform accurate single-step-ahead predictions under...
Thomas Buchert
1993-09-30T23:59:59.000Z
The Lagrangian perturbation theory on Friedman-Lemaitre cosmologies investigated and solved up to the second order in earlier papers (Buchert 1992, Buchert \\& Ehlers 1993) is evaluated up to the third order. On its basis a model for non-linear clustering applicable to the modeling of large-scale structure in the Universe for generic initial conditions is formulated. A truncated model is proposed which represents the ``main body'' of the perturbation sequence in the early non-linear regime by neglecting all gravitational sources which describe interaction of the perturbations. However, I also give the irrotational solutions generated by the interaction terms to the third order, which induce vorticity in Lagrangian space. The consequences and applicability of the solutions are put into perspective. In particular, the model presented enables the study of previrialization effects in gravitational clustering and the onset of non-dissipative gravitational turbulence within the cluster environment.
Lhallabi, T.; Saidi, E.H.
1988-03-01T23:59:59.000Z
D = 2 N = (4, 4) harmonic superspace analysis is developed. The underlying untwisted (4, 4) non linear sigma-models are studied. A method of deriving chiral (4, 0) and (0, 4) models is presented. The Lagrange superparameter used to put the constraint specifying the hyperkahler manifold structure is predicted and its relation to the matter superfield is stated in a covariant way. A known construction is recovered. The authors show also that (4, 4) model is not a direct sum of its chiral ones. Finally a twisted (4, 4-bar) model is obtained.
Is the gasoline tax regressive?
Poterba, James M.
1990-01-01T23:59:59.000Z
Claims of the regressivity of gasoline taxes typically rely on annual surveys of consumer income and expenditures which show that gasoline expenditures are a larger fraction of income for very low income households than ...
Investigation of a Linear Model to Describe Hydrologic Phenomenon of Drainage Basins
Schmer, F. A.
1969-01-01T23:59:59.000Z
This investigation is concerned with the applicability of the linear convolution relationship for approximating the rainfall-runoff phenomenon for small drainage basins. A solution for the transfer function of the convolution relationship...
On a three-layer Hele-Shaw model of enhanced oil recovery with a linear viscous profile
Daripa, Prabir; Meneses, Rodrigo
2015-01-01T23:59:59.000Z
We present a non-standard eigenvalue problem that arises in the linear stability of a three-layer Hele-Shaw model of enhanced oil recovery. A nonlinear transformation is introduced which allows reformulation of the non-standard eigenvalue problem as a boundary value problem for Kummer's equation when the viscous profile of the middle layer is linear. Using the existing body of works on Kummer's equation, we construct an exact solution of the eigenvalue problem and provide the dispersion relation implicitly through the existence criterion for the non-trivial solution. We also discuss the convergence of the series solution. It is shown that this solution reduces to the physically relevant solutions in two asymptotic limits: (i) when the linear viscous profile approaches a constant viscous profile; or (ii) when the length of the middle layer approaches zero.
Discussion of possible evidence for non-linear BCS resistance in SRF cavity data to model comparison
Bauer, P.; Solyak, N.; /Fermilab; Ciovati, G.L.; /Jefferson Lab; Eremeev, G.; /Cornell U., Phys. Dept.; Gurevich, A.; /Wisconsin U., Madison; Lilje, L.; /DESY; Visentin, B.; /Saclay
2005-07-01T23:59:59.000Z
Very powerful RF cavities are now being developed for future large-scale particle accelerators such as the International Linear Collider (ILC). The basic model for the cavity quality factor Q-slope in high gradient SRF cavities, i.e. the reduction of Q with increasing operating electric and magnetic fields, is the so-called thermal feedback model (TFBM). Most important for the agreement between the model and experimental data, however, is which different surface resistance contributions are included in the TFBM. This paper attempts to further clarify if the non-linear pair-breaking correction to the BCS resistance [1,2] is among those surface resistance contributions, through a comparison of TFBM calculations with experimental data from bulk Nb cavities built and tested at several different laboratories.
An integrated 6 MV linear accelerator model from electron gun to dose in a water tank
St Aubin, J.; Steciw, S.; Kirkby, C.; Fallone, B. G. [Department of Physics, University of Alberta, 11322-89 Avenue, Edmonton, Alberta T6G 2G7 (Canada) and Department of Oncology, Medical Physics Division, University of Alberta, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada); Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada) and Department of Oncology, Medical Physics Division, University of Alberta, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada); Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada); Department of Physics, University of Alberta, 11322-89 Avenue, Edmonton, Alberta T6G 2G7 (Canada); Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada) and Department of Oncology, Medical Physics Division, University of Alberta, 11560 University Avenue, Edmonton, Alberta T6G 1Z2 (Canada)
2010-05-15T23:59:59.000Z
Purpose: The details of a full simulation of an inline side-coupled 6 MV linear accelerator (linac) from the electron gun to the target are presented. Commissioning of the above simulation was performed by using the derived electron phase space at the target as an input into Monte Carlo studies of dose distributions within a water tank and matching the simulation results to measurement data. This work is motivated by linac-MR studies, where a validated full linac simulation is first required in order to perform future studies on linac performance in the presence of an external magnetic field. Methods: An electron gun was initially designed and optimized with a 2D finite difference program using Child's law. The electron gun simulation served as an input to a 6 MV linac waveguide simulation, which consisted of a 3D finite element radio-frequency field solution within the waveguide and electron trajectories determined from particle dynamics modeling. The electron gun design was constrained to match the cathode potential and electron gun current of a Varian 600C, while the linac waveguide was optimized to match the measured target current. Commissioning of the full simulation was performed by matching the simulated Monte Carlo dose distributions in a water tank to measured distributions. Results: The full linac simulation matched all the electrical measurements taken from a Varian 600C and the commissioning process lead to excellent agreements in the dose profile measurements. Greater than 99% of all points met a 1%/1mm acceptance criterion for all field sizes analyzed, with the exception of the largest 40x40 cm{sup 2} field for which 98% of all points met the 1%/1mm acceptance criterion and the depth dose curves matched measurement to within 1% deeper than 1.5 cm depth. The optimized energy and spatial intensity distributions, as given by the commissioning process, were determined to be non-Gaussian in form for the inline side-coupled 6 MV linac simulated. Conclusions: An integrated simulation of an inline side-coupled 6 MV linac has been completed and benchmarked matching all electrical and dosimetric measurements to high accuracy. The results showed non-Gaussian spatial intensity and energy distributions for the linac modeled.
Masuda, H.; Claridge, D.
2012-01-01T23:59:59.000Z
, cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...
Meta-Analysis for Longitudinal Data Models using Multivariate Mixture Priors
West, Mike
of multivariate normals, accomodating population heterogeneity, out- liers and non-linearity in regression. First, the random e#11;ects model is a exible mixture of multivariate normals, accomodating population
Masuda, H.; Claridge, D.
2012-01-01T23:59:59.000Z
, cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...
Logistic Regression and Artificial Neural Networks for Classification of Ovarian Tumors
Logistic Regression and Artificial Neural Networks for Classification of Ovarian Tumors C. Lu1 , J to generate and evaluate both logistic regression models and artificial neural network (ANN) models to predict, including explorative univariate and multivariate analysis, and the development of the logistic regression
Piecewise Linear Instrumental Variable Estimation of Causal Influence Richard Scheines
Spirtes, Peter
studies show that when the causal influence of X on Y is non-linear, the piecewise linear linear IV-estimator. In the final section, we describe an experiment comparing regular regression, linearPiecewise Linear Instrumental Variable Estimation of Causal Influence Richard Scheines Dept
Stuart, Andrew
Kalman filtering and smoothing for linear wave equations with model error This article has been:10.1088/0266-5611/27/9/095008 Kalman filtering and smoothing for linear wave equations with model an online approach to state estimation inverse problems when data are acquired sequentially. The Kalman
Regression analysis of oncology drug licensing deal values
Hawkins, Paul Allen
2006-01-01T23:59:59.000Z
This work is an attempt to explain wide variations in drug licensing deal value by using regression modeling to describe and predict the relationship between oncology drug deal characteristics and their licensing deal ...
Marcus Hutter -1 -Bayesian Regression of Piecewise Constant Functions Bayesian Regression of
Hutter, Marcus
Marcus Hutter - 1 - Bayesian Regression of Piecewise Constant Functions Bayesian Regression6 June 2006 #12;Marcus Hutter - 2 - Bayesian Regression of Piecewise Constant Functions Table of Contents · Bayesian Regression · Quantities of Interest · Efficient Solutions by Dynamic Programming · Determination
Qiu Zicheng; Wang Xiangzhao; Bi Qunyu; Yuan Qiongyan; Peng Bo; Duan Lifeng
2009-07-01T23:59:59.000Z
A linear measurement model of lithographic projection lens aberrations is studied numerically based on the Hopkins theory of partially-coherent imaging and positive resist optical lithography (PROLITH) simulation. In this linearity model, the correlation between the mark's structure and its sensitivities to aberrations is analyzed. A method to design a mark with high sensitivity is proved and declared. By use of this method, a translational-symmetry alternating phase shifting mask (Alt-PSM) grating mark is redesigned with all of the even orders, {+-}3rd and {+-}5th order diffraction light missing. In the evaluation simulation, the measurement accuracies of aberrations prove to be enhanced apparently by use of the redesigned mark instead of the old ones.
Hart, W.E.; Istrail, S. [Sandia National Labs., Albuquerque, NM (United States). Algorithms and Discrete Mathematics Dept.
1996-08-09T23:59:59.000Z
This paper considers the protein structure prediction problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven extremely useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. The authors consider two side chain models: a lattice model that generalizes the HP model (Dill 85) to explicitly represent side chains on the cubic lattice, and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. They describe algorithms for both of these models with mathematically guaranteed error bounds. In particular, the authors describe a linear time performance guaranteed approximation algorithm for the HP side chain model that constructs conformations whose energy is better than 865 of optimal in a face centered cubic lattice, and they demonstrate how this provides a 70% performance guarantee for the HP-TSSC model. This is the first algorithm in the literature for off-lattice protein structure prediction that has a rigorous performance guarantee. The analysis of the HP-TSSC model builds off of the work of Dancik and Hannenhalli who have developed a 16/30 approximation algorithm for the HP model on the hexagonal close packed lattice. Further, the analysis provides a mathematical methodology for transferring performance guarantees on lattices to off-lattice models. These results partially answer the open question of Karplus et al. concerning the complexity of protein folding models that include side chains.
Edwards, Lloyd [USDA Forest Service, Southern Research Station] [USDA Forest Service, Southern Research Station; Parresol, Bernie [USDA Forest Service, Southern Research Station] [USDA Forest Service, Southern Research Station
2012-09-17T23:59:59.000Z
The primary research objective of the project is to determine an optimum model to spatially interpolate point derived tree site index (SI). This optimum model will use relevant data from 635 measured sample points to create continuous 40 meter SI raster layer of entire study extent.
Regression analysis with missing data
Michelli, Frank Anthony
1968-01-01T23:59:59.000Z
: Statistios REGRESSION ANALYSIS WITH MISS1NG DATA A Thesis FRANK ANTHONY MICHELLI Approved as to style and content by: hairman of o ttee Member Head of Department Member Member Zanuary 196B ACZNOWLED ONE NT S I can only begin to express my sincere...
Christopher Beetle; Benjamin Bromley; Richard H. Price
2006-02-08T23:59:59.000Z
The periodic standing wave approach to binary inspiral assumes rigid rotation of gravitational fields and hence helically symmetric solutions. To exploit the symmetry, numerical computations must solve for ``helical scalars,'' fields that are functions only of corotating coordinates, the labels on the helical Killing trajectories. Here we present the formalism for describing linearized general relativity in terms of helical scalars and we present solutions to the mixed partial differential equations of the linearized gravity problem (and to a toy nonlinear problem) using the adapted coordinates and numerical techniques previously developed for scalar periodic standing wave computations. We argue that the formalism developed may suffice for periodic standing wave computations for post-Minkowskian computations and for full general relativity.
Towards Analytic Solutions of Step-Wise Safe Switching for Known Affine-Linear Models
Koumboulis, Fotis N.; Tzamtzi, Maria P. [Department of Automation, Halkis Institute of Technology, 34400 Psahna, Evia (Greece)
2008-09-17T23:59:59.000Z
In the present work we establish conditions which guarantee safe transitions for the closed-loop system produced by the application of the Step-Wise Safe Switching control approach to an affine linear system when the nonlinear description of the plant is known. These conditions are based on the local Input to State Stability (ISS) properties of the nonlinear system around the plant's nominal operating points.
Blandin, Sebastien
2012-01-01T23:59:59.000Z
in the case of non-linear regression since there is nocan be extended to non-linear regression methods through the
Simultaneous regression and clustering to predict movie ratings
Rodriguez, Matthew
2010-01-01T23:59:59.000Z
Testing the Regression Coefficients . . . . . . . . .2 Performing Logistic Regression on the Movielens dataset2.4 Logistic Regression Experiments . . . . . . . . . .
36-220 Lab #11 Multiple Regression
Spirtes, Peter
36-220 Lab #11 Multiple Regression Please write your name below, tear off this front page and give;36-220 Lab #11 Multiple Regression First let's download the dataset we will need for the lab. Download gas Simple Regression Analysis 1. Fit the least squares regression line to describe the relationship between
ContentsContents4343Regression and correlation
Vickers, James
ContentsContents4343Regression and correlation 1. Regression 2. Correlation Learning outcomes You. 1 #12;Regression 43.1 Introduction Problems in engineering often involve the exploration of the relationship(s) between two or more variables. The technique of regression analysis is very useful and well
Jr, Peter Chien; Rosenman, Karla; Cheung, Wang; Wang, Nadia; Sanchez, Miguel
2009-01-01T23:59:59.000Z
type 1/type 2 mosaic of psoriasis? Dermatology 2006; 212:Magalhaes RF, et al. Linear psoriasis in Brazilian childrensuffering from linear psoriasis along lines of Blaschko. Br
Muendej, Krisanee
2004-11-15T23:59:59.000Z
Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply ...
Bounding A Protein's Free Energy In Lattice Models Via Linear Programming
Newman, Alantha
useful abstractions in understanding protein structure. In these models, a protein folds to maximize H of protein folding in the Hydrophobic- Hydrophilic (HP) model. The widely-studied HP model was introduced by Ken Dill [5, 6]. This model abstracts the dominant force in protein folding: the hydrophobic
A Modeling and Filtering Framework for Linear Differential-Algebraic Equations
Schön, Thomas
, Dymola, the SimMechanics toolbox for MATLAB, and Modelica [14], [20]. Such modeling software makes
Shell Element Verification & Regression Problems for DYNA3D
Zywicz, E
2008-02-01T23:59:59.000Z
A series of quasi-static regression/verification problems were developed for the triangular and quadrilateral shell element formulations contained in Lawrence Livermore National Laboratory's explicit finite element program DYNA3D. Each regression problem imposes both displacement- and force-type boundary conditions to probe the five independent nodal degrees of freedom employed in the targeted formulation. When applicable, the finite element results are compared with small-strain linear-elastic closed-form reference solutions to verify select aspects of the formulations implementation. Although all problems in the suite depict the same geometry, material behavior, and loading conditions, each problem represents a unique combination of shell formulation, stabilization method, and integration rule. Collectively, the thirty-six new regression problems in the test suite cover nine different shell formulations, three hourglass stabilization methods, and three families of through-thickness integration rules.
IBLStreams: A System for Instance-Based Classification and Regression on Data Streams
Hüllermeier, Eyke
IBLStreams: A System for Instance-Based Classification and Regression on Data Streams Ammar Shaker to classification and regression problems. In comparison to model-based methods for learning on data streams. Keywords: Data streams, classification, regression, instance-based learn- ing, concept drift. 1 #12
Autologistic Regression Analysis of Spatial-Temporal Binary Data via Monte Carlo
Aukema, Brian
Autologistic Regression Analysis of Spatial-Temporal Binary Data via Monte Carlo Maximum Likelihood regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial- temporal autologistic regression model and draw statistical inference via
Estimating Salinity between 25 and 45S in the Atlantic Ocean Using Local Regression
Estimating Salinity between 25° and 45°S in the Atlantic Ocean Using Local Regression W. C. THACKER and temperature in the South Atlantic is quantified with the aid of local regression. To capture the spatial fitting regression models to the profile data is considerably more work than using published clima
Q. Baghi; G. Métris; J. Bergé; B. Christophe; P. Touboul; M. Rodrigues
2015-03-04T23:59:59.000Z
The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method which cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive (AR) fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whose goal is to test the Weak Equivalence Principle (WEP) with a precision of $10^{-15}$. In this particular context the signal of interest is the WEP violation signal expected to be found around a well defined frequency. We test our method with different gap patterns and noise of known PSD and find that the results agree with the mission requirements, decreasing the uncertainty by a factor 60 with respect to ordinary least squares methods. We show that it also provides a test of significance to assess the uncertainty of the measurement.
Longitudinal Control Of A Platoon Of Vehicles. I, Linear Model (ucb/erl M89/106)
Sheikholeslam, Shahab; Desoer, Charles A.
1989-01-01T23:59:59.000Z
We propose the following linear control law for longitudinalgoing to use the proposed linear control law for the firstgoing to use the proposed linear control law for the second
Son, Kiyoung
2012-07-16T23:59:59.000Z
The objective of this study is to develop a quantifying model that predicts the appraised unit value of parcels in San Francisco County based on number of LEED-NC Public Transportation Access (PTA) qualified bus, light rail and commuter rail stops...
A trajectory piecewise-linear approach to model order reduction of nonlinear dynamical systems
RewieÅ„ ski, MichaÅ‚ Jerzy, 1975-
2003-01-01T23:59:59.000Z
(cont.) Finally, we present projection schemes which result in improved accuracy of the reduced order TPWL models, as well as discuss approaches leading to guaranteed stable and passive TPWL reduced-order models.
ORIGINAL ARTICLE A Constitutive Model For the Warp-Weft Coupled Non-linear
Reddy, Batmanathan Dayanand "Daya"
) on the development of airship fabrics. However, the first real model for fabric forces was presented by Peirce (1937
Williamson, John
models, taking into account their uncertainty. The approach is applied to a simulated wheel slip control task illustrating controller development based on a nonparametric model of the unknown friction of the nonlinear models' derivatives. I. INTRODUCTION Robust control is a fairly mature field, in particular
Regression analysis with longitudinal measurements
Ryu, Duchwan
2005-08-29T23:59:59.000Z
, in the cardiotoxic effects of doxorubicin chemotherapy for the treat- ment of acute lymphoblastic leukemia in childhood (Lipsitz et al., 2002; Fitzmaurice et al., 2003), the design points are not pre-defined but determined by the preceding response. This outcome...-dependent feature of measurements makes biased estimation of regression line. As noticed by Lipsitz et al. (2002); Fitzmaurice et al. (2003), even the least square estimates will be biased, which does not require the distributional assumption of response error...
Partial least squares regression as an alternative to current regression methods used in ecology
Carrascal, Luis M.
Partial least squares regression as an alternative to current regression methods used in ecology regression analysis (PLSR), and its potential utility in ecological studies. This statistical technique with multiple regression (MR) and with a combination of principal component analysis and multiple regression
Leung, L.C. [Chinese Univ. of Hong Kong, Shatin (Hong Kong). Decision Science and Managerial Economics] [Chinese Univ. of Hong Kong, Shatin (Hong Kong). Decision Science and Managerial Economics; Khator, S.K. [Univ. of South Florida, Tampa, FL (United States). Industrial and Management Systems Engineering] [Univ. of South Florida, Tampa, FL (United States). Industrial and Management Systems Engineering
1995-05-01T23:59:59.000Z
The Power Delivery Substation Division at Florida Power and Light (FPL) must plan and provide logistical support for about 2,000 transformers located at roughly 400 substations. Each year, to meet new transformer requirements, the Division must make the decision of procuring and/or relocating transformers. Due to the large number of transformers and substations involved, there is a strong need for a systematic approach to determine optimally the decisions for transformer procurement and relocation, as well as their schedules. In this paper, a mixed 0-1 linear programming model is developed for that purpose.
Lionheart, Bill
MATH38141 Regression Analysis - Exam, January 2015 - Feedback General I felt that many errors could a simple regression model instead of the general regression model given on page 7 of the lecture notes tests showed that the simple regression model was not good, but the quadratic was. Hence, the latter
Dynamic Retrospective Regression for Functional Daniel Gervini
Gervini, Daniel
synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear counts over time in HIV patients can be mod- eled as functions of viral load trajectories (Liang et al. 2003, Wu and Liang 2004, Wu and Müller 2011); gene expression profiles of insects at the pupal stage
Testing for a Linear MA Model against Threshold MA Models Author(s): Shiqing Ling and Howell Tong
Ling, Shiqing
. Testing the threshold in nonstationary AR models was investigated by Caner and Hansen [7]. The asymptotic
Bounding A Protein's Free Energy In Lattice Models Via Linear Programming
Newman, Alantha
in understanding protein structure prediction. In these models, a protein folds to maximize H-H contacts (minimize [4], abstracts the dominant force in protein folding: the hydrophobic interaction. The hydrophobicity of protein folding in the Hydro- phobic-Hydrophilic (HP) model. We formulate several di#11;erent integer
Least-Order Torsion-Gravity for Fermion Fields, and the Non-Linear Potentials in the Standard Models
Luca Fabbri
2014-12-15T23:59:59.000Z
We will consider the least-order torsional completion of gravity for a spacetime filled with fermionic Dirac matter fields, and we study the effects of the background-induced non-linear potentials for the matter field themselves in view of their effects for both standard models of physics: from the one of cosmology to that of particles, we will discuss the mechanisms of generation of the cosmological constant and particle masses as well as the phenomenology of leptonic weak-like forces and neutrino oscillations, the problem of zero-point energy, how there can be neutral massive fields as candidates for dark matter, and avoidance of gravitational singularity formation; we will show the way in which all these different effects can nevertheless be altogether described in terms of just a single model, which will be thoroughly discussed in the end.
Minimum of $?/s$ and the phase transition of the Linear Sigma Model in the large-N limit
Antonio Dobado; Felipe J. Llanes-Estrada; Juan M. Torres-Rincon
2009-12-03T23:59:59.000Z
We reexamine the possibility of employing the viscosity over entropy density ratio as a diagnostic tool to identify a phase transition in hadron physics to the strongly coupled quark-gluon plasma and other circumstances where direct measurement of the order parameter or the free energy may be difficult. It has been conjectured that the minimum of eta/s does indeed occur at the phase transition. We now make a careful assessment in a controled theoretical framework, the Linear Sigma Model at large-N, and indeed find that the minimum of eta/s occurs near the second order phase transition of the model due to the rapid variation of the order parameter (here the sigma vacuum expectation value) at a temperature slightly smaller than the critical one.
Non-linear load-deflection models for seafloor interaction with steel catenary risers
Jiao, Yaguang
2009-05-15T23:59:59.000Z
or attached to the riser would be washed away. 10 2.1.4 Model Tests of Steel Catenary Riser A full scale mode test of a steel catenary riser was conducted as part of the STRIDE III JIP, by 2H Offshore Engineering Ltd to investigate the effects of fluid...) developed advanced soil stiffness and soil suction models using STRIDE and CARISIMA JIP test data and other published literature data. This newer model describes the load-deflection response of the soil-pipe interaction associated with the riser vertical...
Regression quantiles for time series
Cai, Zongwu
2002-02-01T23:59:59.000Z
~see, e+g+, Ibragimov and Linnik, 1971, p+ 316!+ Namely, partition REGRESSION QUANTILES FOR TIME SERIES 187 $1, + + + , n% into 2qn 1 1 subsets with large block of size r 5 rn and small block of size s 5 sn+ Set q 5 qn 5 ? n rn 1 sn? , (A.7) where {x...! are the standard Lindeberg–Feller conditions for asymptotic normality of Qn,1 for the independent setup+ Let us first establish ~A+8!+ To this effect, we define the large-block size rn by rn 5 {~nhn!102} and the small-block size sn 5 {~nhn!1020log n}+ Then, as n r...
TEA - a linear frequency domain finite element model for tidal embayment analysis
Westerink, Joannes J.
1984-01-01T23:59:59.000Z
A frequency domain (harmonic) finite element model is developed for the numerical prediction of depth average circulation within small embayments. Such embayments are often characterized by irregular boundaries and bottom ...
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Petkos, Georgios; Toussaint, Marc; Vijayakumar, Sethu
For stationary systems, efficient techniques for adaptive motor control exist which learn the system’s inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often ...
Faghaninia, Alireza; Lo, Cynthia S
2015-01-01T23:59:59.000Z
Accurate models of carrier transport are essential for describing the electronic properties of semiconductor materials. To the best of our knowledge, the current models following the framework of the Boltzmann transport equation (BTE) either rely heavily on experimental data (i.e., semi-empirical), or utilize simplifying assumptions, such as the constant relaxation time approximation (BTE-cRTA). While these models offer valuable physical insights and accurate calculations of transport properties in some cases, they often lack sufficient accuracy -- particularly in capturing the correct trends with temperature and carrier concentration. We present here a general transport model for calculating low-field electrical drift mobility and Seebeck coefficient of n-type semiconductors, by explicitly considering all relevant physical phenomena (i.e. elastic and inelastic scattering mechanisms). We first rewrite expressions for the rates of elastic scattering mechanisms, in terms of ab initio properties, such as the ban...
Thresholding Multivariate Regression and Generalized Principal Components
Sun, Ranye
2014-03-17T23:59:59.000Z
the curse of dimensionality. It is desirable to estimate the regression coefficient matrix by low-rank matrices constructed from its SVD. We reduce such regression problems to sparse SVD problems for cor- related data matrices and generalize the fast...
Becker, Jörg D
2015-01-01T23:59:59.000Z
In many European countries the growth of the real GDP per capita has been linear since 1950. An explanation for this linearity is still missing. We propose that in artificial intelligence we may find models for a linear growth of performance. We also discuss possible consequences of the fact that in systems with linear growth the percentage growth goes to zero.
Polynomial regression with derivative information in nuclear reactor uncertainty quantification*
Anitescu, Mihai
parameters on the performance of a model of sodium-cooled fast reactor. The experiments show-cooled fast reactor. We construct a surrogate model as a goal-oriented projection onto an incomplete space1 Polynomial regression with derivative information in nuclear reactor uncertainty quantification
Modeling the Non-linear Viscoelastic Response of High Temperature Polyimides
Karra, Satish
2010-01-01T23:59:59.000Z
A constitutive model is developed to predict the viscoelastic response of polyimide resins that are used in high temperature applications. This model is based on a thermodynamic framework that uses the notion that the `natural configuration' of a body evolves as the body undergoes a process and the evolution is determined by maximizing the rate of entropy production in general and the rate of dissipation within purely mechanical considerations. We constitutively prescribe forms for the specific Helmholtz potential and the rate of dissipation (which is the product of density, temperature and the rate of entropy production), and the model is derived by maximizing the rate of dissipation with the constraint of incompressibility, and the reduced energy dissipation equation is also regarded as a constraint in that it is required to be met in every process that the body undergoes. The efficacy of the model is ascertained by comparing the predictions of the model with the experimental data for PMR-15 and HFPE-II-52 ...
Modeling the Non-linear Viscoelastic Response of High Temperature Polyimides
Satish Karra; K. R. Rajagopal
2010-08-20T23:59:59.000Z
A constitutive model is developed to predict the viscoelastic response of polyimide resins that are used in high temperature applications. This model is based on a thermodynamic framework that uses the notion that the `natural configuration' of a body evolves as the body undergoes a process and the evolution is determined by maximizing the rate of entropy production in general and the rate of dissipation within purely mechanical considerations. We constitutively prescribe forms for the specific Helmholtz potential and the rate of dissipation (which is the product of density, temperature and the rate of entropy production), and the model is derived by maximizing the rate of dissipation with the constraint of incompressibility, and the reduced energy dissipation equation is also regarded as a constraint in that it is required to be met in every process that the body undergoes. The efficacy of the model is ascertained by comparing the predictions of the model with the experimental data for PMR-15 and HFPE-II-52 polyimide resins.
Nonparametric Regression with Correlated Errors Jean Opsomer
Wang, Yuedong
Nonparametric Regression with Correlated Errors Jean Opsomer Iowa State University Yuedong Wang Nonparametric regression techniques are often sensitive to the presence of correlation in the errors splines and wavelet regression under correlation, both for short-range and long-range dependence
Regression on feature projections H. Altay Guvenir
Güvenir, H. Altay
Regression on feature projections H. Altay Guvenir * Uysal a Department Computer Engineering ######## This paper describes machine learning method, called Regression Feature Projections (RFP), predicting real with KNN based-regression algorithms. Results real sets achieves or comparable accuracy and is both Rule
SUBSPACE REGRESSION IN REPRODUCING KERNEL HILBERT SPACE
SUBSPACE REGRESSION IN REPRODUCING KERNEL HILBERT SPACE L. Hoegaerts #3; J.A.K. Suykens #3; J: We focus on three methods for finding a suitable subspace for regression in a reproducing kernel correlation analysis and we demonstrate how this fits within a more general context of subspace regression
Lee, Shiu-Hang; Kamae, Tuneyoshi; Ellison, Donald C.
2008-07-02T23:59:59.000Z
We present a 3-dimensional model of supernova remnants (SNRs) where the hydrodynamical evolution of the remnant is modeled consistently with nonlinear diffusive shock acceleration occurring at the outer blast wave. The model includes particle escape and diffusion outside of the forward shock, and particle interactions with arbitrary distributions of external ambient material, such as molecular clouds. We include synchrotron emission and cooling, bremsstrahlung radiation, neutral pion production, inverse-Compton (IC), and Coulomb energy-loss. Boardband spectra have been calculated for typical parameters including dense regions of gas external to a 1000 year old SNR. In this paper, we describe the details of our model but do not attempt a detailed fit to any specific remnant. We also do not include magnetic field amplification (MFA), even though this effect may be important in some young remnants. In this first presentation of the model we don't attempt a detailed fit to any specific remnant. Our aim is to develop a flexible platform, which can be generalized to include effects such as MFA, and which can be easily adapted to various SNR environments, including Type Ia SNRs, which explode in a constant density medium, and Type II SNRs, which explode in a pre-supernova wind. When applied to a specific SNR, our model will predict cosmic-ray spectra and multi-wavelength morphology in projected images for instruments with varying spatial and spectral resolutions. We show examples of these spectra and images and emphasize the importance of measurements in the hard X-ray, GeV, and TeV gamma-ray bands for investigating key ingredients in the acceleration mechanism, and for deducing whether or not TeV emission is produced by IC from electrons or pion-decay from protons.
Ahn, Hongshik
-dimensional data using the logistic regression model as a base clas- sifier. CERP is similar to random subspace (Ho classification problems using ensem- bles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit
VIDEO REALISTIC TALKING HEADS USING HIERARCHICAL NON-LINEAR SPEECH-APPEARANCE MODELS
Martin, Ralph R.
of muscles in the face, mouth and neck [1] and realistic animations must in- clude movement of the tongue-articulated realistic facial synthesis. Nat- ural mouth and face dynamics are learned in training to allow new facial the appearance of a speaker's mouth and face are modelled separately and combined to produce the final video
Some Useful Matlab and Control Systems Toolbox Functions Creating and converting linear models
Abate, Alessandro
). step - Step response. impulse - Impulse response. lsim - Response to arbitrary inputs. bode - Bode-zero map. damp - Natural frequency and damping of system poles. ltiview - Response analysis GUI (LTI Viewer diagrams of the frequency response. ctrb - Controllability matrix (for ss models). obsv - Observability
Non-Linear Drying Diffusion and Viscoelastic Drying Shrinkage Modeling in Hardened Cement Pastes
Leung, Chin K.
2010-07-14T23:59:59.000Z
The present research seeks to study the decrease in diffusivity rate as relative humidity (RH) decreases and modeling drying shrinkage of hardened cement paste as a poroviscoelastic respose. Thin cement paste strips of 0.4 and 0.5 w/c at age 3 and 7...
Maximum Likelihood Estimation for Probit-Linear Mixed Models with Correlated Random Effects
Du, Jie
Jennifer S. K. Chan and Anthony Y. C. Kuk Department of Statistics, University of New South Wales, Sydney 2052, Australia The probit-normal model for binary data (McCulloch, 1994, Journal of the American function, one has to integrate out the random effects, which, except for a few special cases, cannot
Linear-quadratic model predictive control for urban traffic , Hai L. Vu a
Nazarathy, Yoni
Accepted 30 June 2013 Keywords: Model predictive control Intelligent transport system Congestion control- tion systems are driving the field of intelligent transport systems (ITS) into the twenty first century for large urban networks containing thousands of sensors and actuators. We demonstrate the essence of our
Giuseppe D'Adamo; Andrea Pelissetto; Carlo Pierleoni
2014-09-18T23:59:59.000Z
A coarse-graining strategy, previously developed for polymer solutions, is extended here to mixtures of linear polymers and hard-sphere colloids. In this approach groups of monomers are mapped onto a single pseudoatom (a blob) and the effective blob-blob interactions are obtained by requiring the model to reproduce some large-scale structural properties in the zero-density limit. We show that an accurate parametrization of the polymer-colloid interactions is obtained by simply introducing pair potentials between blobs and colloids. For the coarse-grained model in which polymers are modelled as four-blob chains (tetramers), the pair potentials are determined by means of the iterative Boltzmann inversion scheme, taking full-monomer pair correlation functions at zero-density as targets. For a larger number $n$ of blobs, pair potentials are determined by using a simple transferability assumption based on the polymer self-similarity. We validate the model by comparing its predictions with full-monomer results for the interfacial properties of polymer solutions in the presence of a single colloid and for thermodynamic and structural properties in the homogeneous phase at finite polymer and colloid density. The tetramer model is quite accurate for $q\\lesssim 1$ ($q=\\hat{R}_g/R_c$, where $\\hat{R}_g$ is the zero-density polymer radius of gyration and $R_c$ is the colloid radius) and reasonably good also for $q=2$. For $q=2$ an accurate coarse-grained description is obtained by using the $n=10$ blob model. We also compare our results with those obtained by using single-blob models with state-dependent potentials.
Linear Dependence and Linear Independence
PRETEX (Halifax NS) #1 1054 1999 Mar 05 10:59:16
2010-02-12T23:59:59.000Z
Feb 16, 2007 ... Observe that the vector (1, 2) is already a linear combination of (1, 0) and (0, 1), and therefore it does not add any new vectors to the linear span ...
Adjoint-based linear analysis in reduced order thermo-acoustic models
Magri, Luca; Juniper, Matthew P.
2014-09-23T23:59:59.000Z
Instabilities in Gas Turbine Engines: Operational Experience, Fundamental Mechanisms, and Modeling. American Institute of Aeronautics and Astronautics, Inc., 2005. [3] F. E. C. Culick. Unsteady motions in combustion chambers for propulsion systems. RTO... flames. Journal of Engineering of Gas Turbines and Power, 2012, 134:031502. [28] L. Kabiraj and R. I. Sujith. Nonlinear self-excited thermoacoustic oscillations: intermittency and flame blow-out. Journal of Fluid Mechanics, 2012, 713:376–397. [29] K...
The SUSY seesaw model and lepton-flavor violation at a future electron-positron linear collider
F. Deppisch; H. Päs; A. Redelbach; R. Rückl; Y. Shimizu
2004-05-11T23:59:59.000Z
We study lepton-flavor violating slepton production and decay at a future e^+e^- linear collider in context with the seesaw mechanism in mSUGRA post-LEP benchmark scenarios. The present knowledge in the neutrino sector as well as improved future measurements are taken into account. We calculate the signal cross-sections \\sigma(e^{+/-}e^- -> l_{\\beta}^{+/-} l_{\\alpha}^- \\tilde{\\chi}_b^0 \\tilde{\\chi}_a^0); l_{\\delta}=e, \\mu, \\tau; \\alpha =|= \\beta and estimate the main background processes. Furthermore, we investigate the correlations of these signals with the corresponding lepton-flavor violating rare decays l_{\\alpha} -> l_{\\beta} \\gamma. It is shown that these correlations are relatively weakly affected by uncertainties in the neutrino data, but very sensitive to the model parameters. Hence, they are particularly suited for probing the origin of lepton-flavor violation.
Robust Regression Appendix to An R and S-PLUS Companion to Applied Regression
Masci, Frank
Robust Regression Appendix to An R and S-PLUS Companion to Applied Regression John Fox January 2002 regression, is to employ a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation, introduced by Huber (1964).1 Consider
Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression
Gelman, Andrew
Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression Comparing Andrew Gelman Some Recent Progress in Simple Statistical Methods #12;Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression Comparing the upper third to the lower third
Reynolds, Jacob G. [Washington River Protection Solutions, LLC, Richland, WA (United States)
2013-01-11T23:59:59.000Z
Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a change in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH){sub 4}-H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results determined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.
Goodness-of-Fit Test Issues in Generalized Linear Mixed Models
Chen, Nai-Wei
2012-02-14T23:59:59.000Z
checking of Case 1 for (1)ZSm and (2)cS tran m . . . 58 13 Results of the type I error rate of Sm by using local polynomial smoothed residuals are computed based on the scaled chi-squared distribution cSm...-cluster interaction term of fixed effects between two con- tinuous covariates when the alternative model (4.6) is assumed. . . . 64 17 Results of controlling type I error rate of Sm by using local poly- nomial smoothed residuals are computed based on cSm when...
Hyper-Fit: Fitting Linear Models to Multidimensional Data with Multivariate Gaussian Uncertainties
Robotham, A S G
2015-01-01T23:59:59.000Z
Astronomical data is often uncertain with errors that are heteroscedastic (different for each data point) and covariant between different dimensions. Assuming that a set of D-dimensional data points can be described by a (D - 1)-dimensional plane with intrinsic scatter, we derive the general likelihood function to be maximised to recover the best fitting model. Alongside the mathematical description, we also release the hyper-fit package for the R statistical language (github.com/asgr/hyper.fit) and a user-friendly web interface for online fitting (hyperfit.icrar.org). The hyper-fit package offers access to a large number of fitting routines, includes visualisation tools, and is fully documented in an extensive user manual. Most of the hyper-fit functionality is accessible via the web interface. In this paper we include applications to toy examples and to real astronomical data from the literature: the mass-size, Tully-Fisher, Fundamental Plane, and mass-spin-morphology relations. In most cases the hyper-fit ...
Quantum Energy Regression using Scattering Transforms
Matthew Hirn; Nicolas Poilvert; Stephane Mallat
2015-02-06T23:59:59.000Z
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It possesses appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.
Quantum Energy Regression using Scattering Transforms
Hirn, Matthew; Mallat, Stephane
2015-01-01T23:59:59.000Z
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It possesses appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.
Tian, Zhen; Li, Yongbao; Shi, Feng; Jiang, Steve B; Jia, Xun
2015-01-01T23:59:59.000Z
We recently built an analytical source model for GPU-based MC dose engine. In this paper, we present a sampling strategy to efficiently utilize this source model in GPU-based dose calculation. Our source model was based on a concept of phase-space-ring (PSR). This ring structure makes it effective to account for beam rotational symmetry, but not suitable for dose calculations due to rectangular jaw settings. Hence, we first convert PSR source model to its phase-space let (PSL) representation. Then in dose calculation, different types of sub-sources were separately sampled. Source sampling and particle transport were iterated. So that the particles being sampled and transported simultaneously are of same type and close in energy to alleviate GPU thread divergence. We also present an automatic commissioning approach to adjust the model for a good representation of a clinical linear accelerator . Weighting factors were introduced to adjust relative weights of PSRs, determined by solving a quadratic minimization ...
Tang, Robert Y., E-mail: rx-tang@laurentian.ca [Biomolecular Sciences Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada); Laamanen, Curtis, E-mail: cx-laamanen@laurentian.ca; McDonald, Nancy, E-mail: mcdnancye@gmail.com [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada)] [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada); LeClair, Robert J., E-mail: rleclair@laurentian.ca [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6, Canada and Biomolecular Sciences Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada)
2014-05-15T23:59:59.000Z
Purpose: Develop a method to subtract fat tissue contributions to wide-angle x-ray scatter (WAXS) signals of breast biopsies in order to estimate the differential linear scattering coefficients ?{sub s} of fatless tissue. Cancerous and fibroglandular tissue can then be compared independent of fat content. In this work phantom materials with known compositions were used to test the efficacy of the WAXS subtraction model. Methods: Each sample 5 mm in diameter and 5 mm thick was interrogated by a 50 kV 2.7 mm diameter beam for 3 min. A 25 mm{sup 2} by 1 mm thick CdTe detector allowed measurements of a portion of the ? = 6° scattered field. A scatter technique provided means to estimate the incident spectrum N{sub 0}(E) needed in the calculations of ?{sub s}[x(E, ?)] where x is the momentum transfer argument. Values of ?{sup ¯}{sub s} for composite phantoms consisting of three plastic layers were estimated and compared to the values obtained via the sum ?{sup ¯}{sub s}{sup ?}(x)=?{sub 1}?{sub s1}(x)+?{sub 2}?{sub s2}(x)+?{sub 3}?{sub s3}(x), where ?{sub i} is the fractional volume of the ith plastic component. Water, polystyrene, and a volume mixture of 0.6 water + 0.4 polystyrene labelled as fibphan were chosen to mimic cancer, fat, and fibroglandular tissue, respectively. A WAXS subtraction model was used to remove the polystyrene signal from tissue composite phantoms so that the ?{sub s} of water and fibphan could be estimated. Although the composite samples were layered, simulations were performed to test the models under nonlayered conditions. Results: The well known ?{sub s} signal of water was reproduced effectively between 0.5 < x < 1.6 nm{sup ?1}. The ?{sup ¯}{sub s} obtained for the heterogeneous samples agreed with ?{sup ¯}{sub s}{sup ?}. Polystyrene signals were subtracted successfully from composite phantoms. The simulations validated the usefulness of the WAXS models for nonlayered biopsies. Conclusions: The methodology to measure ?{sub s} of homogeneous samples was quantitatively accurate. Simple WAXS models predicted the probabilities for specific x-ray scattering to occur from heterogeneous biopsies. The fat subtraction model can allow ?{sub s} signals of breast cancer and fibroglandular tissue to be compared without the effects of fat provided there is an independent measurement of the fat volume fraction ?{sub f}. Future work will consist of devising a quantitative x-ray digital imaging method to estimate ?{sub f} in ex vivo breast samples.
Struchtrup, Henning
466]) for kinetic equations. The basic idea is to use a linearized expression of the reference distribution function in the kinetic equation, instead of its exact expression, in the numerical scheme. This modified scheme. Therefore, kinetic models have been proposed with simplified expressions for the collision term
Deselaers, Thomas
A GIS-LIKE TRAINING ALGORITHM FOR LOG-LINEAR MODELS WITH HIDDEN VARIABLES Georg Heigold, Thomas with Generalized Iterative Scal- ing (GIS). GIS offers, upon others, the immediate advantages that it is locally convergent, completely parameter free, and guarantees an improvement of the criterion in each step. GIS
Regression of Environmental Noise in LIGO Data
Vaibhav Tiwari; Marco Drago; Valery Frolov; Sergey Klimenko; Guenakh Mitselmakher; Valentin Necula; Giovanni Prodi; Virginia Re; Francesco Salemi; Gabriele Vedovato; Igor Yakushin
2015-03-25T23:59:59.000Z
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.
Regression of Environmental Noise in LIGO Data
Tiwari, Vaibhav; Frolov, Valery; Klimenko, Sergey; Mitselmakher, Guenakh; Necula, Valentin; Prodi, Giovanni; Re, Virginia; Salemi, Francesco; Vedovato, Gabriele; Yakushin, Igor
2015-01-01T23:59:59.000Z
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.
W. B. Vasantha Kandasamy; Florentin Smarandache
2008-07-18T23:59:59.000Z
In this book, the authors introduce the notion of Super linear algebra and super vector spaces using the definition of super matrices defined by Horst (1963). This book expects the readers to be well-versed in linear algebra. Many theorems on super linear algebra and its properties are proved. Some theorems are left as exercises for the reader. These new class of super linear algebras which can be thought of as a set of linear algebras, following a stipulated condition, will find applications in several fields using computers. The authors feel that such a paradigm shift is essential in this computerized world. Some other structures ought to replace linear algebras which are over a century old. Super linear algebras that use super matrices can store data not only in a block but in multiple blocks so it is certainly more powerful than the usual matrices. This book has 3 chapters. Chapter one introduces the notion of super vector spaces and enumerates a number of properties. Chapter two defines the notion of super linear algebra, super inner product spaces and super bilinear forms. Several interesting properties are derived. The main application of these new structures in Markov chains and Leontief economic models are also given in this chapter. The final chapter suggests 161 problems mainly to make the reader understand this new concept and apply them.
Stout, Quentin F.
2008-01-01T23:59:59.000Z
In Computational Statistics and Data Analysis 53 (2008), pp. 289297 Unimodal Regression via Prefix Isotonic Regression Quentin F. Stout University of Michigan Ann Arbor, MI 481092121 Abstract This paper gives algorithms for determining real-valued uni- variate unimodal regressions, that is, for determining
A new sliced inverse regression method for multivariate response regression
Paris-Sud XI, Université de
) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. We discuss the choice of the dimension of the EDR space. Moreover, we provide a way to cluster components of y related to the same EDR space. One can thus
A new sliced inverse regression method for multivariate response regression
reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. We discuss the choice of the dimension of the EDR space. The numerical a way to cluster components of y related to the same EDR space. One can thus apply properly multivariate
Support vector methods for survival analysis: a comparison between ranking and regression
techniques for the estimation of non-linear transformation models for the analysis of survival data. Methods is the use of non-linear kernels im- plementing automatically non-parametric effects of the covariates the advantage that they are easily extendable towards non-linear models without the need to check non-linearities
Fike, Jeffrey A.
2013-08-01T23:59:59.000Z
The construction of stable reduced order models using Galerkin projection for the Euler or Navier-Stokes equations requires a suitable choice for the inner product. The standard L2 inner product is expected to produce unstable ROMs. For the non-linear Navier-Stokes equations this means the use of an energy inner product. In this report, Galerkin projection for the non-linear Navier-Stokes equations using the L2 inner product is implemented as a first step toward constructing stable ROMs for this set of physics.
ANALYTICAL EMISSION MODELS FOR SIGNALISED ARTERIALS Bruce Hellinga, Mohammad Ali Khan, and Liping Fu
Hellinga, Bruce
ANALYTICAL EMISSION MODELS FOR SIGNALISED ARTERIALS Bruce Hellinga, Mohammad Ali Khan, and Liping for quantifying vehicle tailpipe emissions. In this paper we present non-linear regression models that can be used for emission data is examined using field data. The proposed models have adjusted R 2 values ranging from 0
Cambridge, University of
of reality. Neural networks form a general method of non{linear regression. Their exibility enables them non{linear function is tted to experimental data, Fig 4.1 as in linear regression, the input variable are derived. The general form of the equation developed using linear regression is a sum of the products
Sadat Hayatshahi, Sayyed Hamed [Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of) ; Abdolmaleki, Parviz [Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of) ]. E-mail: parviz@modares.ac.ir; Safarian, Shahrokh [Department of Biology, Faculty of Science, Tehran University, P.O. Box: 13155-6455, Tehran (Iran, Islamic Republic of) ; Khajeh, Khosro [Department of Biochemistry, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of)
2005-12-16T23:59:59.000Z
Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, the previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.
Kissock, Kelly
% of the world's energy consumption (Boyle 2004). The use of fossil fuels is the primary contributor to global, and production data to understand a company's energy performance over time. The method uses regression modelingUnderstanding Industrial Energy Use through Sliding Regression Analysis Carl W. Eger III, City
Design of active suspension control based upon use of tubular linear motor and quarter-car model
Allen, Justin Aaron
2008-10-10T23:59:59.000Z
The design, fabrication, and testing of a quarter-car facility coupled with various control algorithms are presented in this thesis. An experimental linear tubular motor, capable of producing a 52-N force, provides control ...
Abdel Nasser Tawfik; Niseem Magdy
2015-01-06T23:59:59.000Z
Effects of external magnetic field on various properties of the quantum chromodynamics under extreme conditions of temperature and density have been analysed. To this end, we use SU(3) Polyakov linear sigma-model and assume that the external magnetic field eB adds some restrictions to the quarks energy due to the existence of free charges in the plasma phase. In doing this, we apply the Landau theory of quantization. This requires an additional temperature to drive the system through the chiral phase-transition. Accordingly, the dependence of the critical temperature of chiral and confinement phase-transitions on the magnetic field is characterized. Based on this, we have studied the thermal evolution of thermodynamic quantities and the first four higher-order moment of particle multiplicity. Having all these calculations, we have studied the effects of magnetic field on chiral phase-transition. We found that both critical temperature T_c and critical chemical potential increase with increasing the magnetic field eB. Last but not least, the magnetic effects of the thermal evolution of four scalar and four pseudoscalar meson states are studied. We concluded that the meson masses decrease as the temperature increases till T_c. Then, the vacuum effect becomes dominant and rapidly increases with the temperature T. At low T, the scalar meson masses normalized to the lowest Matsubara frequency rapidly decreases as T increases. Then, starting from T_c, we find that the thermal dependence almost vanishes. Furthermore, the meson masses increase with increasing magnetic field. This gives characteristic phase diagram of T vs. external magnetic field $B. At high T, we find that the masses of almost all meson states become temperature independent. It is concluded that the various meson states likely have different T_c's.
Southworth, Frank [ORNL; Garrow, Dr. Laurie [Georgia Institute of Technology
2011-01-01T23:59:59.000Z
This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.
Jann, Dominic 1983-
2012-08-17T23:59:59.000Z
as predictors to a Bayesian logistic regression model in lieu of a restrictive binary structure yields marginal improvement over current methodologies....
A Library for Locally Weighted Projection Regression
Klanke, Stefan; Vijayakumar, Sethu; Schaal, Stefan
2008-01-01T23:59:59.000Z
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our ...
Spatial variation decomposition via sparse regression
Zhang, Wangyang
In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) ...
DYNA3D/ParaDyn Regression Test Suite Inventory
Lin, J I
2011-01-25T23:59:59.000Z
The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of production release 10.1 in September 2010. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark in the corresponding column. The definition of ''feature'' has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to a particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds, compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.
Christov, Ivan C.
-Champaign, Illinois June 3, 2009 * Travel funding from the organizers is kindly acknowledged. Ivan Christov (NU RankineHugoniot jump conditions for the nonlinear equations, nonlinear shock speed and an ad-hoc solution-dependent conductivity. 2 Solution of the linearized equations, singular surface theory results. 3 RankineHugoniot jump
Hobert, James P.
Statistics with S (4th edition, 2002), Springer. We will use the statistical computing language R (which can at Chapter 4. If you prefer to use other statistical languages or statistical packages and do not intend level; Â· a one-year sequence in theoretical statistics at the graduate level; Â· a course in linear
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
IRT Goodness-of-Fit Using Approaches from Logistic Regression
Mair, Patrick; Reise, Steven P.; Bentler, Peter M.
2008-01-01T23:59:59.000Z
connect IRT and logistic regression, we represent the matrixApproaches from Logistic Regression us write the log-oddsApproaches from Logistic Regression Cox DR, Snell EJ (1989).
IRT Goodness-of-Fit Using Approaches from Logistic Regression
Patrick Mair; Steven P. Reise; Peter M. Bentler
2011-01-01T23:59:59.000Z
connect IRT and logistic regression, we represent the matrixApproaches from Logistic Regression us write the log-oddsApproaches from Logistic Regression Cox DR, Snell EJ (1989).
Special set linear algebra and special set fuzzy linear algebra
W. B. Vasantha Kandasamy; Florentin Smarandache; K. Ilanthenral
2009-12-30T23:59:59.000Z
The authors in this book introduce the notion of special set linear algebra and special set fuzzy Linear algebra, which is an extension of the notion set linear algebra and set fuzzy linear algebra. These concepts are best suited in the application of multi expert models and cryptology. This book has five chapters. In chapter one the basic concepts about set linear algebra is given in order to make this book a self contained one. The notion of special set linear algebra and their fuzzy analogue is introduced in chapter two. In chapter three the notion of special set semigroup linear algebra is introduced. The concept of special set n-vector spaces, n greater than or equal to three is defined and their fuzzy analogue is their fuzzy analogue is given in chapter four. The probable applications are also mentioned. The final chapter suggests 66 problems.
PAVEMENT PREDICTION PERFORMANCE MODELS AND RELATION WITH TRAFFIC FATALITIES AND INJURIES
Boyer, Edmond
PAVEMENT PREDICTION PERFORMANCE MODELS AND RELATION WITH TRAFFIC FATALITIES AND INJURIES V. CEREZO.gothie@developpement-durable.gouv.fr ABSTRACT This paper presents some results of a study, which aimed at modelling pavement evolution, pavement characteristics and age. In a second part, non-linear regressions were used in view of obtaining
Neural network modelling of hot deformation of austenite
Cambridge, University of
Neural network modelling of hot deformation of austenite Mathew Peet Wolfson College University Neural Networks Neural networks to predict constitutive behaviour 6 6 7 17 20 Experimental Detail 21. Linear regression techniques are not capable of representing the data, however neural networks