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SAS for Mixed Models (Second Edition)

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David A. Dickey
Professor of Statistics, North Carolina State University

This revision of an already excellent text includes information-rich examples serving as templates for a wide variety of applications.

Geert Molenberghs
Center for Statistics, Hasselt University, Diepenbeek, Belgium

The book is ideal for readers familiar with the basic models and wanting to deepen their understanding of modeling concepts.

The indispensable, up-to-date guide to mixed models using SAS. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures in this valuable edition of the comprehensive mixed models guide for data analysis, completely revised and updated for SAS9. The theory underlying the models, the forms of the models for various applications, and a wealth of examples from different fields of study are integrated in the discussions of these models:
  • random effect only and random coefficients models
  • split-plot, multilocation, and repeated measures models
  • hierarchical models with nested random effects
  • analysis of covariance models
  • spatial correlation models
  • generalized linear mixed models
  • nonlinear mixed models
About the Author
Ramon C. Littell, Ph.D., Professor of Statistics at the University of Florida, is the coauthor of several books, including SAS System for Regression, Third Edition, and SAS for Linear Models, Fourth Edition. He has worked with SAS software since 1986. George A. Milliken, Ph.D., Professor of Statistics at Kansas State University, has been using SAS software since 1974 and has extensive experience with the design and analysis of experiments using mixed models by incorporating the GLM, MIXED, GLIMMIX, and NLMIXED procedures. Walter W. Stroup, Ph.D., Professor and Chair of the Department of Statistics at the University of Nebraska, is the coauthor of SAS for Linear Models, Fourth Edition. He has been using SAS software since 1981. Russell D. Wolfinger, Ph.D., is the Director of Scientific Discovery and Genomics at SAS Institute, where he has worked since 1989. Before leading SAS scientific efforts, he authored the MIXED, MULTTEST, KDE, and NLMIXED procedures in SAS/STAT. Oliver Schabenberger, Ph.D., is a Senior Research Statistician at SAS Institute and has been using SAS software since 1991. He maintains and develops mixed model software and is the author of the GLIMMIX procedure.
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