Amy Shi introduces you to the SAS procedure, PROC BGLIMM. BGLIMM stands for Bayesian generalized linear mixed models. It is a Bayesian procedure designed specifically for fitting generalized linear mixed models. It is simpler than the MCMC procedure. It runs efficient sampling algorithms in parallel for fast performance. It enables you to model missing data, nested or nonnested multilevel models, and repeated-measures data.

Amy Shi is a research statistician developer who is part of the Advanced Analytics division at SAS.

Content Outline
00:00 – Introducing the BGLIMM Procedure
00:28 – Mixed Models
01:19 – GLMM Models
02:12 – Features: Simple Syntax
02:58 – Additional Features of PROC BGLIMM
04:22 – Example: Logistic Regression with Random Effects
07:10 – Posterior Summaries and Intervals
08:03 – TAD (Trace, Autocorrelation, Density) Plots
08:23 – ESTIMATE Statement
09:06 – Functions of Parameters
09:51 – Summary
10:30 – For More Information (see links below)

Related Resource
◉ Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models – [ Ссылка ]
◉ PROC BGLIMM requires SAS/STAT 15.1 (SAS 9.4M6). See complete documentation of the procedure – [ Ссылка ]
◉ Additional coding examples – [ Ссылка ]


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