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Synopsis: An accessible and self-contained introduction to statisticalmodels-now in a modernized new edition

Generalized, Linear, and Mixed Models, Second Editionprovides an up-to-date treatment of the essential techniques fordeveloping and applying a wide variety of statistical models. Thebook presents thorough and unified coverage of the theory behindgeneralized, linear, and mixed models and highlights theirsimilarities 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 awide array of contexts. In addition, a discussion of generalmethods for the analysis of such models is presented with anemphasis on the method of maximum likelihood for the estimation ofparameters. The authors also provide comprehensive coverage of thelatest statistical models for correlated, non-normally distributeddata. Thoroughly updated to reflect the latest developments in thefield, the Second Edition features:

  • A new chapter that covers omitted covariates, incorrect randomeffects distribution, correlation of covariates and random effects,and robust variance estimation
  • A new chapter that treats shared random effects models, latentclass models, and properties of models
  • A revised chapter on longitudinal data, which now includes adiscussion of generalized linear models, modern advances inlongitudinal data analysis, and the use between and withincovariate decompositions
  • Expanded coverage of marginal versus conditional models
  • Numerous new and updated examples

With its accessible style and wealth of illustrative exercises,Generalized, Linear, and Mixed Models, Second Edition is anideal book for courses on generalized linear and mixed models atthe upper-undergraduate and beginning-graduate levels. It alsoserves as a valuable reference for applied statisticians,industrial practitioners, and researchers.

About the Author: Charles E. McCulloch, PhD, is Professor and Head of theDivision of Biostatistics in the School of Medicine at theUniversity of California, San Francisco. A Fellow of the AmericanStatistical Association, Dr. McCulloch is the author of numerouspublished articles in the areas of longitudinal data analysis,generalized linear mixed models, and latent class models and theirapplications.

Shayle R. Searle, PhD, is Professor Emeritus in theDepartment of Biological Statistics and Computational Biology atCornell University. Dr. Searle is the author of LinearModels, Linear Models for Unbalanced Data, MatrixAlgebra Useful for Statistics, and Variance Components,all published by Wiley.

John M. Neuhaus, PhD, is Professor of Biostatistics inthe School of Medicine at the University of California, SanFrancisco. A Fellow of the American Statistical Association and theRoyal Statistical Society, Dr. Neuhaus has authored or coauthorednumerous journal articles on statistical methods for analyzingcorrelated response data and assessments on the effects ofstatistical model misspecification.

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Book Description John Wiley and Sons Ltd, United States, 2008. Hardback. Condition: New. 2nd Edition. Language: English . Brand New Book. An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed 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 comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimationA new chapter that treats shared random effects models, latent class models, and properties of modelsA 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 decompositionsExpanded coverage of marginal versus conditional modelsNumerous new and updated examples With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers. Seller Inventory # AAH9780470073711

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Book Description John Wiley and Sons Ltd, United States, 2008. Hardback. Condition: New. 2nd Edition. Language: English . Brand New Book. An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed 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 comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimationA new chapter that treats shared random effects models, latent class models, and properties of modelsA 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 decompositionsExpanded coverage of marginal versus conditional modelsNumerous new and updated examples With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers. Seller Inventory # AAH9780470073711

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