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A modern perspective on mixed models presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. A variety of statistical methods are explained and illustrated.
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)
Preface. Introduction. One--Way Classifications. Single--Predictor Regression. Linear Models (LMs). Generalized Linear Models (GLMs). Linear Mixed Models (LMMs). Longitudinal Data. GLMMs. Prediction. Computing. Nonlinear Models. Appendix M: Some Matrix Results. Appendix S: Some Statistical Results. References. Index.
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CHARLES E. MCCULLOCH, PhD, is Professor of Biostatistics at the University of California, San Francisco. He is the author of numerous scientific publications on biometrics and biological statistics and a coauthor (with Shayle Searle and George Casella) of Variance Components (Wiley). SHAYLE R. SEARLE, PhD, is Professor Emeritus of Biometry at Cornell University. He is the author of Linear Models, Linear Models for Unbalanced Data, and Matrix Algebra Useful for Statistics, all from Wiley.
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