Random Effect And Latent Variable Model Selection

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Random Effect and Latent Variable Model Selection

David Dunson
Random Effect and Latent Variable Model Selection Book PDF

Author : David Dunson
Publisher : Springer Science & Business Media
Page : 174 pages
File Size : 46,7 Mb
Release : 2010-03-18
Category : Mathematics
ISBN : 9780387767215

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Random Effect and Latent Variable Model Selection by David Dunson Pdf

Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Random Effect and Latent Variable Model Selection

David Dunson
Random Effect and Latent Variable Model Selection Book PDF

Author : David Dunson
Publisher : Springer
Page : 170 pages
File Size : 51,6 Mb
Release : 2010-11-16
Category : Mathematics
ISBN : 0387568395

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Random Effect and Latent Variable Model Selection by David Dunson Pdf

Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Mixed Effects Models for Complex Data

Lang Wu
Mixed Effects Models for Complex Data Book PDF

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 47,6 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Bayesian Hierarchical Models

Peter D. Congdon
Bayesian Hierarchical Models Book PDF

Author : Peter D. Congdon
Publisher : CRC Press
Page : 506 pages
File Size : 44,6 Mb
Release : 2019-09-16
Category : Mathematics
ISBN : 9780429532900

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Bayesian Hierarchical Models by Peter D. Congdon Pdf

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Generalized Latent Variable Modeling

Anders Skrondal,Sophia Rabe-Hesketh
Generalized Latent Variable Modeling Book PDF

Author : Anders Skrondal,Sophia Rabe-Hesketh
Publisher : CRC Press
Page : 528 pages
File Size : 48,7 Mb
Release : 2004-05-11
Category : Mathematics
ISBN : 9780203489437

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Generalized Latent Variable Modeling by Anders Skrondal,Sophia Rabe-Hesketh Pdf

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi

Latent Variable Modeling and Applications to Causality

Maia Berkane
Latent Variable Modeling and Applications to Causality Book PDF

Author : Maia Berkane
Publisher : Springer Science & Business Media
Page : 285 pages
File Size : 47,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461218425

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Latent Variable Modeling and Applications to Causality by Maia Berkane Pdf

This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.

Advances in Latent Variable Mixture Models

Gregory R. Hancock,Karen M. Samuelsen
Advances in Latent Variable Mixture Models Book PDF

Author : Gregory R. Hancock,Karen M. Samuelsen
Publisher : IAP
Page : 385 pages
File Size : 51,9 Mb
Release : 2007-11-01
Category : Mathematics
ISBN : 9781607526346

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Advances in Latent Variable Mixture Models by Gregory R. Hancock,Karen M. Samuelsen Pdf

The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a “lay of the land” for latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems, deals with mixtures for data that are hierarchical in nature either due to the data’s sampling structure or to the repetition of measures (of varied types) over time. Part II, Models for Assessment and Diagnosis, addresses scenarios for making judgments about individuals’ state of knowledge or development, and about the instruments used for making such judgments. Finally, Part III, Challenges in Model Evaluation, focuses on some of the methodological issues associated with the selection of models most accurately representing the processes and populations under investigation. It should be stated that this volume is not intended to be a first exposure to latent variable methods. Readers lacking such foundational knowledge are encouraged to consult primary and/or secondary didactic resources in order to get the most from the chapters in this volume. Once armed with the basic understanding of latent variable methods, we believe readers will find this volume incredibly exciting.

Applied Bayesian Hierarchical Methods

Peter D. Congdon
Applied Bayesian Hierarchical Methods Book PDF

Author : Peter D. Congdon
Publisher : CRC Press
Page : 606 pages
File Size : 52,6 Mb
Release : 2010-05-19
Category : Mathematics
ISBN : 9781584887218

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Applied Bayesian Hierarchical Methods by Peter D. Congdon Pdf

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach

Handbook of Psychology, Research Methods in Psychology

Irving B. Weiner,John A. Schinka,Wayne F. Velicer
Handbook of Psychology Research Methods in Psychology Book PDF

Author : Irving B. Weiner,John A. Schinka,Wayne F. Velicer
Publisher : John Wiley & Sons
Page : 806 pages
File Size : 42,6 Mb
Release : 2012-10-16
Category : Psychology
ISBN : 9780470890646

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Handbook of Psychology, Research Methods in Psychology by Irving B. Weiner,John A. Schinka,Wayne F. Velicer Pdf

Psychology is of interest to academics from many fields, as well as to the thousands of academic and clinical psychologists and general public who can't help but be interested in learning more about why humans think and behave as they do. This award-winning twelve-volume reference covers every aspect of the ever-fascinating discipline of psychology and represents the most current knowledge in the field. This ten-year revision now covers discoveries based in neuroscience, clinical psychology's new interest in evidence-based practice and mindfulness, and new findings in social, developmental, and forensic psychology.

Advances in Latent Variables

Maurizio Carpita,Eugenio Brentari,El Mostafa Qannari
Advances in Latent Variables Book PDF

Author : Maurizio Carpita,Eugenio Brentari,El Mostafa Qannari
Publisher : Springer
Page : 285 pages
File Size : 46,9 Mb
Release : 2015-04-01
Category : Mathematics
ISBN : 9783319029672

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Advances in Latent Variables by Maurizio Carpita,Eugenio Brentari,El Mostafa Qannari Pdf

The book, belonging to the series “Studies in Theoretical and Applied Statistics– Selected Papers from the Statistical Societies”, presents a peer-reviewed selection of contributions on relevant topics organized by the editors on the occasion of the SIS 2013 Statistical Conference "Advances in Latent Variables. Methods, Models and Applications", held at the Department of Economics and Management of the University of Brescia from June 19 to 21, 2013. The focus of the book is on advances in statistical methods for analyses with latent variables. In fact, in recent years, there has been increasing interest in this broad research area from both a theoretical and an applied point of view, as the statistical latent variable approach allows the effective modeling of complex real-life phenomena in a wide range of research fields. A major goal of the volume is to bring together articles written by statisticians from different research fields, which present different approaches and experiences related to the analysis of unobservable variables and the study of the relationships between them.

Bayesian Disease Mapping

Andrew B. Lawson
Bayesian Disease Mapping Book PDF

Author : Andrew B. Lawson
Publisher : CRC Press
Page : 464 pages
File Size : 41,9 Mb
Release : 2018-05-20
Category : Mathematics
ISBN : 9781351271752

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Bayesian Disease Mapping by Andrew B. Lawson Pdf

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.

Handbook of Item Response Theory

Wim J. van der Linden
Handbook of Item Response Theory Book PDF

Author : Wim J. van der Linden
Publisher : CRC Press
Page : 454 pages
File Size : 50,7 Mb
Release : 2017-03-31
Category : Mathematics
ISBN : 9781315362816

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Handbook of Item Response Theory by Wim J. van der Linden Pdf

Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.

Current Trends in Bayesian Methodology with Applications

Satyanshu K. Upadhyay,Umesh Singh,Dipak K. Dey,Appaia Loganathan
Current Trends in Bayesian Methodology with Applications Book PDF

Author : Satyanshu K. Upadhyay,Umesh Singh,Dipak K. Dey,Appaia Loganathan
Publisher : CRC Press
Page : 674 pages
File Size : 51,7 Mb
Release : 2015-05-21
Category : Mathematics
ISBN : 9781482235128

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Current Trends in Bayesian Methodology with Applications by Satyanshu K. Upadhyay,Umesh Singh,Dipak K. Dey,Appaia Loganathan Pdf

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples. This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.

Advances in Genetics

Anonim
Advances in Genetics Book PDF

Author : Anonim
Publisher : Academic Press
Page : 139 pages
File Size : 40,7 Mb
Release : 2013-02-14
Category : Science
ISBN : 9780124078031

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Advances in Genetics by Anonim Pdf

The field of genetics is rapidly evolving, and new medical breakthroughs are occurring as a result of advances in our knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Includes methods for testing with ethical, legal, and social implications Critically analyzes future

Bayesian Statistics 9

José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman,Adrian F. M. Smith,Mike West
Bayesian Statistics 9 Book PDF

Author : José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman,Adrian F. M. Smith,Mike West
Publisher : Oxford University Press
Page : 128 pages
File Size : 43,5 Mb
Release : 2011-10-06
Category : Mathematics
ISBN : 9780191627859

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Bayesian Statistics 9 by José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman,Adrian F. M. Smith,Mike West Pdf

The Valencia International Meetings on Bayesian Statistics - established in 1979 and held every four years - have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the authors(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, high lighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology. The Valencia 9 invited papers cover a broad range of topics, including foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.