Bayesian Variable Selection in Linear and Semiparametric Models

• Activity Number: 541
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Hongmei Zhang*+ and Xianzheng (Shan) Huang and Arnab Maity and Hasan Arshad and Tara Sabo-Attwood and Wilfried Karmaus
• Companies: University of South Carolina and University of South Carolina-Columbia and North Carolina State University and University of Southampton, UK and University of Florida and University of Memphis
• Keywords: Reproducing kernels ; Dirichlet process ; variable selection

Abstract

Selecting variables potentially associated with an outcome of interest is an important step toward successes of inferences and correct identification of risk factors. In some situations, the association can be reasonably described by parametric models such as linear regressions. In other situations, the association is non-linear in an unknown form and semi-parametric models are often applied to model the association. In this talk, I present methods developed recently that have the ability to select important variables in the framework of linear models and semi-parametric models. These methods have the ability to deal with mis-measured variables or select variables that involve complex main and interaction effects. The methods will be illustrated using genetic and epigenetic data.