Generalized, Linear, and Mixed Models

商家名称	信用等级	购买信息	订购本书
		Generalized, Linear, and Mixed Models
		Generalized, Linear, and Mixed Models

基本信息·出版社：Wiley-Interscience
·页码：384 页
·出版日期：2008年06月
·ISBN：0470073713
·条形码：9780470073711
·装帧：精装
·正文语种：英语
·丛书名：Wiley Series in Probability and Statistics
·外文书名：广义、线性与混合模型(第2版)

内容简介 在线阅读本书

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.
作者简介

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.
编辑推荐 Review
"...a very good reference book." -- Zentralblatt MATH, Vol. 964, 2001/14

"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

"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)

"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

"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) --This text refers to the Hardcover edition.

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