Generalized, Linear, and Mixed Models
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Forscher im Bereich mathematische Statistik
Verallgemeinerte lineare Modelle gehören zu den wichtigsten Hilfsmitteln der statistischen Analyse. In diesem in sich geschlossenen Band werden lineare und verallgemeinerte lineare Modelle im gleichen Kontext abgehandelt; gemischte Effekte werden ausführlich besprochen. Informativ für alle, die einen Einblick in die Theorie der mathematischen Statistik erhalten wollen und für Anwender von Statistikpaketen! This new edition presents a unified, accessible, and self contained treatment of the latest statistical methods for analyzing correlated, non normally distributed data. The book s unified treatment addresses the needs of applications oriented users of statistical packages and also graduate students in statistics.
Verallgemeinerte lineare Modelle gehören zu den wichtigsten Hilfsmitteln der statistischen Analyse. In diesem in sich geschlossenen Band werden lineare und verallgemeinerte lineare Modelle im gleichen Kontext abgehandelt; gemischte Effekte werden ausführlich besprochen. Informativ für alle, die einen Einblick in die Theorie der mathematischen Statistik erhalten wollen und für Anwender von Statistikpaketen! This new edition presents a unified, accessible, and self contained treatment of the latest statistical methods for analyzing correlated, non normally distributed data. The book s unified treatment addresses the needs of applications oriented users of statistical packages and also graduate students in statistics.
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 estimation
_ A new chapter that treats shared random effects 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
_ Numerous 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.
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 estimation
_ A new chapter that treats shared random effects 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
_ Numerous 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.
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