- Hardcover: 358 pages
- Publisher: Wiley-Interscience; 1 edition (Dec 18 2000)
- Language: English
- ISBN-10: 047119364X
- ISBN-13: 978-0471193647
- Product Dimensions: 23.6 x 16 x 2.3 cm
- Shipping Weight: 612 g
- Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: #743,738 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Details |
Product Description
Review
"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)
Product Description
Wiley Series in Probability and Statistics
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models
Inside This Book
(Learn More)
First Sentence
We begin by describing fixed and random effects models for the one-way classification for both normally and Bernoulli (binary) distributed data. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
We begin by describing fixed and random effects models for the one-way classification for both normally and Bernoulli (binary) distributed data. Read the first page
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Concordance
Browse Sample PagesConcordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
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Most helpful customer reviews
4.0 out of 5 stars
excellent new book covering a wide variety of models,
By
This review is from: Generalized, Linear, and Mixed Models (Hardcover)
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.
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.5 out of 5 stars (2 customer reviews) 33 of 33 people found the following review helpful
5.0 out of 5 stars
nice recent text on mixed models,
By Michael R. Chernick "statman31147" - Published on Amazon.com
This review is from: Generalized, Linear, and Mixed Models (Hardcover)
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. Comment | Permalink 10 of 15 people found the following review helpful
4.0 out of 5 stars
Very good textbook for the statistic model,
By C. Tu - Published on Amazon.com
This review is from: Generalized, Linear, and Mixed Models (Hardcover)
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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