|Wednesday, November 16, 2022; 11:00am|
|Speaker||Dr. Mingan Yang, Assistant Professor, SDSU School of Public Health|
|Title||Bayesian Variable Selection for Mixed Effects Models|
In analysis of a linear model, one of the main objectives is to assess significant predictors of the outcome variables. However, this is quite challenging for linear mixed effects models due to added predictors among the random effects.In this article, we address the problem of joint selection of both fixed effects and random effects in mixed models. We use a stochastic search Gibbs sampler to implement a fully Bayesian approach for variable selection. The approach is illustrated using simulated data and a real example.