||SAS® for Mixed Models: An Introduction|
By Walter W. Stroup, Ph.D., George A. Milliken, Ph.D., Elizabeth A. Claassen and Russell D. Wolfinger, Ph.D.
Anticipated publication date: Third quarter 2017
An indispensable, comprehensive update to both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, SAS® for Mixed Models: An Introduction empowers SAS users with the latest capabilities for a variety of applications featuring the SAS GLIMMIX, MIXED, and NLMIXED procedures. Mixed models are now the mainstream vehicle for analyzing most research data. They are part of the core curriculum in most master's degree programs in statistics and data science, making this guide the ideal textbook or companion manual. Written for professors and instructors of statistics, graduate students, scientists, junior and senior statisticians in business or government—and any other decision makers who use statistics, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS software. In addition, readers with a basic knowledge of linear models and matrix algebra will find additional background that expands their knowledge.
This book includes comprehensive coverage of mixed models tools to help analysts plan new studies. Presentation of the models integrates the underlying theory, the model forms for various applications, and a wealth of examples from different fields of study. Because SAS Proc GLIMMIX is the most comprehensive and sophisticated mixed model software on the market, the authors of SAS for Mixed Models have converted most of their earlier examples from Proc MIXED to Proc GLIMMIX, highlighting Proc GLIMMIX's new features.
This up-to-date book includes greatly expanded coverage of mixed models for non-normal data, and mixed-model-based precision and power analysis. Topics include the following:
- Random-effect-only and random-coefficients models
- Split-plot, multilocation, and repeated measures models
- Hierarchical models with nested random effects
- Analysis of covariance models
- Generalized linear mixed models
No other mixed model book makes consistent use of Proc MIXED, much less Proc GLIMMIX. This go-to examples guide and comprehensive reference on mixed models therefore fills a gap left by other books that are outdated or address theory while remaining almost mute on applications.