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Generalized, Linear, and Mixed Models, 2nd Edition
Generalized, Linear, and Mixed Models, 2nd Edition
Charles E. McCulloch, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 
Shayle R. Searle, Cornell University, Ithaca, NY
John M. Neuhaus, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
ISBN: 978-0-470-07371-1
©2008
384 pages
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Description  |  Author Info  |  Table of Contents  |  New to This Edition  |  Hallmark Features  |  Sample Chapters
Description
This book provides a unified treatment of the use of mixed models for analyzing correlated data.  Models for non-normal data, i.e. binary or count data, and generalized linear and nonlinear models are described and illustrated.  The first few chapters of the book introduce all the major ideas in a context that will be familiar to most students of statistics.  This text provides an accessible treatment of many of the newer statistical models for correlated, non-normally distributed data.  The book's unified treatment addresses the needs of applications-oriented users of statistical packages and also graduate students in statistics.   

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