A0938
Title: Bayesian modal regression based on mixture distributions
Authors: Ray Bai - University of South Carolina (United States) [presenting]
Abstract: Compared to mean regression and quantile regression, the literature on modal regression is very sparse. A unified framework is proposed for Bayesian modal regression based on a family of unimodal distributions indexed by the mode along with other parameters that allow for flexible shapes and tail behaviours. Sufficient conditions for posterior propriety are derived under an improper prior on the mode parameter. Following prior elicitation, regression analysis of simulated data and datasets from several real-life applications are carried out. Besides drawing inferences for covariate effects that are easy to interpret, prediction and model selection under the proposed Bayesian modal regression framework is considered. Evidence from these analyses suggests that the proposed inference procedures are very robust to outliers, enabling one to discover interesting covariate effects missed by mean or median regression and to construct much tighter prediction intervals than those from mean or median regression.
