TITLE: Using SAS PROC BGLIMM and MCMC for Bayesian Analysis of Mixed Models
SPEAKERS: Walter Stroup, University of Nebraska
MODERATOR: Alfred H. Balch
Recent advances in statistical methodology and software capability have made Bayesian analysis of statistical models more accessible to data analysts. As a result, Bayesian methods have become more important. Many academic journals now discourage significance testing in favor of Bayesian inference. More importantly, the ability to use what we know prior to, or in the early stages of an investigation allow us to improve the accuracy and efficiency of statistical analysis. This tutorial introduces the SAS® system BGLIMM and MCMC procedures for Bayesian analysis. PROC BGLIMM uses syntax similar to PROC GLIMMIX to implement linear mixed models (LMMs) and generalized linear mixed models (GLMMs). PROC MCMC uses syntax similar to PROC NLMIXED and can implement non-linear, zero-inflated and semi-parametric mixed models in addition to LMMs and GLMMs. This tutorial uses examples from SAS for Mixed Models: Introduction and Basic Applications (Stroup, et al., 2018) and examples to appear in SAS for Mixed Models: Advanced Applications to introduce these two procedures and show participants what they need to know to get started with the SAS system for Bayesian analysis.
Walt Stroup is Emeritus Professor of Statistics at the University of Nebraska-Lincoln. He served on the University of Nebraska faculty from 1979 until 2020. His responsibilities included teaching statistical modeling, design of experiments, and research specializing in mixed models and their applications in agriculture, natural resources, medical and pharmaceutical sciences, education, and the behavioral sciences. He is the founding chair of Nebraska’s Department of Statistics, and served as chair from 2001 until 2010. In 2020, he received the University of Nebraska’s Outstanding Teaching and Innovative Curriculum Award, the university’s highest teaching honor. He was a member of PQRI’s Stability Shelf-Life Working Group from its inception in 2006 until its disbanding in 2019. He received PQRI’s Excellence in Research award in 2009. He co-authored SAS for Mixed Models, SAS for Linear Models, 4th ed., and authored Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. He has conducted numerous short courses on mixed and generalized linear models for industry and professional organizations in Africa, Europe, Australia and North America. He is a Fellow of the American Statistical Association.