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Generalized, Linear, and Mixed Models
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Artikel-Nr:
9780470073711
Veröffentl:
2008
Seiten:
416
Autor:
Charles E. McCulloch
Gewicht:
700 g
Format:
241x193x36 mm
Serie:
Wiley Series in Probability and Statistics
Sprache:
Englisch
Beschreibung:
Charles E. McCulloch, PhD, is Professor and Head of the Division of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. McCulloch is the author of numerous published articles in the areas of longitudinal data analysis, generalized linear mixed models, and latent class models and their applications.
Shayle R. Searle, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and Variance Components, all published by Wiley.
John M. Neuhaus, PhD, is Professor of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association and the Royal Statistical Society, Dr. Neuhaus has authored or coauthored numerous journal articles on statistical methods for analyzing correlated response data and assessments on the effects of statistical model misspecification.
Shayle R. Searle, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and Variance Components, all published by Wiley.
John M. Neuhaus, PhD, is Professor of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association and the Royal Statistical Society, Dr. Neuhaus has authored or coauthored numerous journal articles on statistical methods for analyzing correlated response data and assessments on the effects of statistical model misspecification.
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.
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!
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!
Preface.
Preface to the First Edition.
1. Introduction.
2. One-Way Classifications.
3. Single-Predictor Regression.
4. Linear Models (LMs).
5. Generalized Linear Models (GLMs).
6. Linear Mixed Models (LMMs).
7. Generalized Linear Mixed Models.
8. Models for Longitudinal data.
9. Marginal Models.
10. Multivariate Models.
11. Nonlinear Models.
12. Departures From Assumptions.
13. Prediction.
14. Computing.
Appendix M: Some Matrix Results.
Appendix S: Some Statistical Results.
References.
Index.
Preface to the First Edition.
1. Introduction.
2. One-Way Classifications.
3. Single-Predictor Regression.
4. Linear Models (LMs).
5. Generalized Linear Models (GLMs).
6. Linear Mixed Models (LMMs).
7. Generalized Linear Mixed Models.
8. Models for Longitudinal data.
9. Marginal Models.
10. Multivariate Models.
11. Nonlinear Models.
12. Departures From Assumptions.
13. Prediction.
14. Computing.
Appendix M: Some Matrix Results.
Appendix S: Some Statistical Results.
References.
Index.
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