B0630
Title: Bayesian mixture models in regression with variable selection
Authors: Aixin Tan - University of Iowa (United States) [presenting]
Abstract: Heterogeneous data are ubiquitous in scientific studies. In regression problems, the response in different subpopulations may be influenced by different subsets of covariates. We propose using mixtures, especially mixtures of finite mixtures (MFM), to model the joint distribution of the response and the covariates. In particular, we adopt a parameterization that explicitly involves vectors of regression coefficients within each subpopulation, each assigned spike-and-slab priors to achieve subpopulation-specific variable selection. MCMC algorithms are used for computing, leading to versatile posterior inferences, such as clustering, individual profiling, and predictions.
