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 Mellon University in 2004.