Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics) | 
 enlarge | Authors: Charles E. Mcculloch, Shayle R. Searle, John M. Neuhaus
Publisher: Wiley-Interscience
Category: Book
List Price: $99.95
Buy New: $71.87
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Rating:  3 reviews
Sales Rank: 201101
Media: Hardcover
Edition: 2
Pages: 384
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.2 x 6.2 x 1
ISBN: 0470073713
Dewey Decimal Number: 519.535
EAN: 9780470073711
Publication Date: June 30, 2008
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Product Description
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.
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| Customer Reviews:
 nice recent text on mixed models January 22, 2008
Michael R. Chernick (Holland PA)
27 out of 27 found this review helpful
This is a very recent and authoritative treatment of classical parametric models, starting with the general linear model and extending to generalized linear models, linear mixed models and finally to generalized linear mixed models. It also has applciations to longitudinal data analysis and prediction problems. Heavy on theory and matrix algebra but not loaded with applications. Good for a graduate course in statistics especially for Ph.D. students. It is concise covering a large range of topics in only 310 pages. An interesting feature is a chapter on computing that deals with Markov chain Monte Carlo methods in some detail.
There is also a brief chapter on nonlinear models (only 5 pages) that includes an example of corn photosynthesis and also the important application to pharmacokinetic models.
The emphasis is on maximum likelihood estimation and its extensions (e.g. restricted maximum likelihood and penalized likelihood and quasi-likelihood). The authors provide an interesting perspective on the non-applicability of analysis of variance techniques in some mixed effects models.
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 excellent new book covering a wide variety of models December 26, 2001
Michael R. Chernick (Malvern, PA)
29 out of 30 found this review helpful
This is a very recent and authoritative treatment of classical parametric models, starting with the general linear model and extending to generalized linear models, linear mixed models and finally to generalized linear mixed models. It also has applciations to longitudinal data analysis and prediction problems. Heavy on theory and matrix algebra but not loaded with applications. Good for a graduate course in statistics especially for Ph.D. students. It is concise covering a large range of topics in only 310 pages. An interesting feature is a chapter on computing that deals with Markov chain Monte Carlo methods in some detail. There is also a brief chapter on nonlinear models (only 5 pages) that includes an example of corn photosynthesis and also the important application to pharmacokinetic models. The emphasis is on maximum likelihood estimation and its extensions (e.g. restricted maximumlikelihood and penalized likelihood and quasi-likelihood). The authors provide an interesting perspective on the non-applicability of analysis of variance techniques in some mixed effects models.
Very good textbook for the statistic model February 4, 2005
C. Tu (Lincoln, Nebraska United States)
6 out of 8 found this review helpful
This is a very good textbook. Since it covers most of important topics in the short pages. Authors assume that readers have the good background in the linear model. So if you have good background in linear model and statistic inference this will be the wonderful book for the statistic student. This is only one problem of this book. It cost toooo much for a poor student! Thus I take one point out.
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