Bayesian Regression for Semicontinuous Data with Variable Selection

• Activity Number: 302
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jinsu Park*+
• Companies: KAIST
• Keywords: Semicontinuous data ; Stochastic Search Variable Selection ; model misspecification bias ; Truncated normal distribution

Abstract

In biomedical research, semi-continuous data frequently arise. Regression analysis for such semi-continuous outcome variable is challenging because of the inappropriateness of the normal error assumption. One naive approach is to fit a normal regression with log-transformed outcome variable. A two-part model has been developed allowing for more flexibility where two regressions model the binary part and the continuous part of the data separately. Alternatively, a truncated normal regression is applicable, which assumes an underlying latent variable exists and follows a normal regression, and it can be extended to zero-inflated truncated normal model. In this research, we compare 4 different methods for semi-continuous outcome regression in various scenarios via simulation studies and examine how inferences are affected by different model specifications. In all methods, we conduct Bayesian inference and develop MCMC algorithms for posterior sampling where the Stochastic Search Variable Selection (SSVS) structure is incorporated facilitating variable selection. We apply the 4 methods to investigate the relationship between impulsivity and hazardous drinking in young adulthood.