Generalized, Linear, and Mixed Models / Edition 2

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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 Editionis 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.

About the Author:
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

About the Author:
Shayle R. Searle, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University

About the Author:
John M. Neuhaus, PhD, is Professor of Biostatistics in the School of Medicine at the University of California, San Francisco

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Editorial Reviews

From the Publisher
"I strongly recommend…[it] for inclusion in math and statistics libraries and in the personal libraries of professional statisticians." (Journal of the American Statistical Association, December 2006)

"…well written and suitable to be a textbook…I enjoyed reading this book and recommend it highly to statisticians." (Journal of Statistical Computation and Simulation, January 2006)

"This text is to be highly recommended as one that provides a modern perspective on fitting models to data." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"For graduate students and?statisticians, McCulloch and Searle begin by reviewing the basics of linear models and linear mixed models..." (SciTech Book News, Vol. 25, No. 4, December 2001)

"...a very good reference book." (Zentralblatt MATH, Vol. 964, 2001/14)

"...another fine contribution to the statistics literature from these respected authors..." (Technometrics, Vol. 45, No. 1, February 2003)

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Product Details

  • ISBN-13: 9780470073711
  • Publisher: Wiley
  • Publication date: 6/30/2008
  • Series: Wiley Series in Probability and Statistics Series , #651
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 424
  • Sales rank: 723,794
  • Product dimensions: 6.00 (w) x 9.30 (h) x 0.90 (d)

Table of Contents

1 Introduction 1
2 One-Way Classifications 28
3 Single-Predictor Regression 71
4 Linear Models (LMs) 113
5 Generalized Linear Models (GLMs) 135
6 Linear Mixed Models (LMMs) 156
7 Longitudinal Data 187
8 GLMMs 220
9 Prediction 247
10 Computing 263
11 Nonlinear Models 286
App Some Matrix Results 291
App: Some Statistical Results 300
References 311
Index 321
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