Generalized, linear, and mixed models / Charles E. McCulloch, Shayle R. Searle, John M. Neuhaus.

By: McCulloch, Charles E Contributor(s): Searle, S. R. (Shayle R.), 1928- | Neuhaus, John M Material type: Text

TextLanguage: English Series: Wiley series in probability and statisticsPublication details: Hoboken, N.J. : Wiley, c2008. Edition: 2nd edDescription: xxv, 384 p. : ill. ; 25 cmISBN: 9780470073711 (cloth); 0470073713 (cloth)Subject(s): Linear models (Statistics)DDC classification: 519.535 LOC classification: QA279 | .M3847 2008Online resources: Publisher description | WorldCat details | Table of contents only | Contributor biographical information

Contents:

TOC One-way classifications -- Single-predictor regression -- Linear models (LMs) -- Generalized linear models (GLMs) -- Linear mixed models (LMMs) - -Generalized linear mixed models (GLMMs) -- Models for longitudinal data -- Marginal models -- Multivariate models - -Nonlinear models -- Departures from assumptions -- Prediction -- Computing.

Summary: This book provides an up-to-date treatment of the techniques for developing and applying a wide variety of statistical models. It presents unified coverage of the theory behind generalized, linear, and mixed media models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide coverage of the latest statistical models for correlated, non-normally distributed data. This second edition features: a new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and variance estimation; a new chapter that treats shared random effected models, latent class models, and properties of models; a revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions; expanded coverage of marginal versus conditional models, and numerous new and updated examples.

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Item type	Current library	Collection	Call number	Copy number	Status	Barcode
Text	EWU Library Reserve Section	Non-fiction	519.535 MCG 2008 (Browse shelf(Opens below))	C-1	Not For Loan	26683
Text	EWU Library Circulation Section	Non-fiction	519.535 MCG 2008 (Browse shelf(Opens below))	C-2	Available	27044
Text	EWU Library Circulation Section	Non-fiction	519.535 MCG 2008 (Browse shelf(Opens below))	C-3	Available	27045

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Previous	519.535 KOC 2000 Continuous multivariate distributions /	519.535 KOI 2014 Introduction to multivariate analysis :	519.535 LIM 1999 Models for repeated measurements /	519.535 MCG 2008 Generalized, linear, and mixed models /	519.535 MCI 2007 Introduction to statistics for biology /	519.535 MUL 2019 Multivariate data analysis /	519.535 NEI 2006 An introduction to copulas /	Next

Includes bibliographical references (p. 364-377) and index.

TOC One-way classifications --
Single-predictor regression --
Linear models (LMs) --
Generalized linear models (GLMs) --
Linear mixed models (LMMs) - -Generalized linear mixed models (GLMMs) --
Models for longitudinal data --
Marginal models --
Multivariate models - -Nonlinear models --
Departures from assumptions --
Prediction --
Computing.

This book provides an up-to-date treatment of the techniques for developing and applying a wide variety of statistical models. It presents unified coverage of the theory behind generalized, linear, and mixed media models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide coverage of the latest statistical models for correlated, non-normally distributed data. This second edition features: a new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and variance estimation; a new chapter that treats shared random effected models, latent class models, and properties of models; a revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions; expanded coverage of marginal versus conditional models, and numerous new and updated examples.

Tahur Ahmed

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