George A. Milliken
Emeritus Professor of Statistics
Kansas State University

George A. Milliken, PhD, is an emeritus professor of statistics at Kansas State University, where he taught for 38 years, and owner of a consulting business that helps researchers with study design, analysis, and reporting. His professional research emphases include linear and nonlinear mixed models and complex study design. He has used SAS software since 1974 and has extensive experience with design and analysis of experiments using mixed models and relying on the SAS GLM, MIXED, GLIMMIX, and NLMIXED procedures. He is a Fellow of the American Statistical Association and a highly cited author of numerous articles in journals of statistics. Dr. Milliken is a coauthor of the three-volume Analysis of Messy Data series—Volume 1: Designed Experiments ; Volume 2: Nonreplicated Experiments ; Volume 3: Analysis of Covariance . In 2016 he received the Dixon Award for Excellence in Statistical Consulting from the American Statistical Association. Dr. Milliken completed his PhD at Colorado State University.

By This Author

SAS® for Mixed Models: An Introduction and Basic Applications

By Walter W. Stroup, George A. Milliken, Elizabeth A. Claassen, and Russell D. Wolfinger

Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, 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.

SAS® for Mixed Models, Second Edition

By Ramon C. Littell, George A. Milliken, Walter W. Stroup, Russell D. Wolfinger,
and Oliver Schabenberger

Describes the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures. Includes random effect only and random coefficients models; split-plot, multi-location, and repeated measures models; hierarchical models with nested random effects; and much more.

Back to Top