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Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings. This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers. The chapters are based on the contributors’ research, with mathematical details minimized using applications-motivated descriptions. The first part of the book focuses on frequentist likelihood ratio and score tests for zero variance components. Contributors include Xihong Lin, Daowen Zhang and Ciprian Crainiceanu. The second part focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models. Contributors include David Dunson and collaborators Bo Cai and Saki Kinney. The final part focuses on structural equation models, with Peter Bentler and Jiajuan Liang presenting a frequentist approach, Sik-Yum Lee and Xin-Yuan Song presenting a Bayesian approach based on path sampling, and Joyee Ghosh and David Dunson proposing a method for default prior specification and efficient posterior computation. David Dunson is Professor in the Department of Statistical Science at Duke University. He is an international authority on Bayesian methods for correlated data, a fellow of the American Statistical Association, and winner of the David Byar and Mortimer Spiegelman Awards.
Statistics. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Electronic books. -- local. --- Latent variables. --- Random data (Statistics). --- Latent variables --- Random data (Statistics) --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Data, Random (Statistics) --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Statistics --- Stochastic processes --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Distribution (Probability theory. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk
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“Decision Systems and Nonstochastic Randomness” presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models. Each self-contained chapter of this neatly-structured monograph includes a detailed introduction and summary of its contents. The included results are presented not only with rigorous proofs but also through numerous intuitive examples. An appendix is provided which includes classic results from the theory of functions and measured sets as well as decision theory, offering an overview of the necessary prerequisites. The formalism of statistical regularities developed in this book will have a significant influence on decision theory and information theory as well as numerous other disciplines. Because of these far-reaching implications, this book may be a useful resource for statisticians, mathematicians, engineers, economists and other utilizing nonstochastic modeling and decision theory.
Random data (Statistics). --- Random dynamical systems. --- Statistical decision. --- Statistical decision --- Random data (Statistics) --- Random dynamical systems --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Dynamical systems, Random --- Data, Random (Statistics) --- Decision problems --- Mathematics. --- Operations research. --- Decision making. --- Business mathematics. --- Applied mathematics. --- Engineering mathematics. --- Game theory. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Business Mathematics. --- Statistical Theory and Methods. --- Game Theory, Economics, Social and Behav. Sciences. --- Operation Research/Decision Theory. --- Differentiable dynamical systems --- Statistics --- Stochastic processes --- Game theory --- Operations research --- Management science --- Distribution (Probability theory. --- Mathematical statistics. --- Operations Research/Decision Theory. --- Operational analysis --- Operational research --- Industrial engineering --- Research --- System theory --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Arithmetic, Commercial --- Business --- Business arithmetic --- Business math --- Commercial arithmetic --- Finance --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistical methods --- Statistics . --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Games, Theory of --- Theory of games --- Mathematical models --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Decision making
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