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 SAS for Mixed Models, Second Edition, the comprehensive mixed models guide for data analysis, completely revised and updated for SAS 9 by authors Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger. 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; and nonlinear mixed models.
Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered.
This book is part of the SAS Press program.
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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.
"With an abundance of new material and a thorough updating of material from the first edition, SAS for Mixed Models, Second Edition will be of inordinate interest to those of us engaged in the modeling of messy continuous and categorical data. It contains several new chapters, and its printed format makes this a much more readable version than its predecessor. We owe the authors a tip of the hat for providing such an invaluable compendium." --Timothy G. Gregoire, J. P. Weyerhaeuser Professor of Forest Management, School of Forestry and Environmental Studies, Yale University
"This extensively updated second edition of SAS for Mixed Models will be an essential reference for anyone involved in mixed modeling. A decade of experience has been incorporated into each chapter with three completely new chapters on mixed model diagnostics, power calculations, and Bayesian approaches. The book is filled with practical advice for dealing with important issues that come up with real data, such as avoiding common pitfalls in modeling, correctly interpreting output, troubleshooting nonlinear mixed model fitting and choosing from among the multiple SAS procedures/macros now available.
The already distinguished group of authors has been further enhanced with the addition of Oliver Schabenberger, the developer of the new GLIMMIX procedure and an expert in spatial modeling and mixed model diagnostics. The procedures that are new since the first edition, NLMIXED and GLIMMIX, are extensively covered. Examples of new graphics and new output from the GLIMMIX procedure are scattered throughout the book because of their usefulness in both standard linear mixed modeling and generalized linear mixed modeling." --Cliff Pereira, Department of Statistics, Oregon State University
With an abundance of new material and a thorough updating of material from the first edition, SAS for Mixed Models, Second Edition will be of inordinate interest to those of us engaged in the modeling of messy continuous and categorical data. It contains several new chapters, and its printed format makes this a much more readable version than its predecessor. We owe the authors a tip of the hat for providing such an invaluable compendium. --Timothy G. Gregoire, J. P. Weyerhaeuser Professor of Forest Management, School of Forestry and Environmental Studies, Yale University
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