In this presentation I will describe how to use two procedures in SAS/STAT, the MCMC procedure and the BGLIMM procedure, to fit Bayesian hierarchical models. Random-effects models are broadly used for data that are hierarchically structured and they offer flexibility that can capture the complex nature in real-world applications. In SAS, you can use the general-purpose PROC MCMC for model exploration and the high-performance PROC BGLIMM for fitting generalized linear mixed models. I will describe how to use these procedures for estimation and inference.

Fang Chen is a Director of Advanced Statistical Methods at SAS Institute Inc. and a Fellow of the American Statistical Association.  He manages the development of statistical software for SAS/STAT®, SAS/QC®, and analytical components that drive SAS® Visual Statistics software.  Also among his responsibilities are the development of Bayesian analysis software and the MCMC procedure. Before joining SAS, he received his Ph.D. in statistics from Carnegie…