Bayesian Modal Regression based on Mixture Distributions

TL;DR

This paper introduces a Bayesian modal regression framework using mixture distributions, providing robust inference, outlier resistance, and improved prediction intervals compared to traditional mean or median regression methods.

Contribution

It proposes a unifying Bayesian modal regression model based on unimodal distributions, with conditions for posterior propriety and practical algorithms for real data analysis.

Findings

Robustness to outliers demonstrated in simulations and real data

Ability to discover covariate effects missed by mean or median regression

Construction of tighter prediction intervals than traditional methods

Abstract

Compared to mean regression and quantile regression, the literature on modal regression is very sparse. A unifying framework for Bayesian modal regression is proposed, based on a family of unimodal distributions indexed by the mode, along with other parameters that allow for flexible shapes and tail behaviors. Sufficient conditions for posterior propriety under an improper prior on the mode parameter are derived. Following prior elicitation, regression analysis of simulated data and datasets from several real-life applications are conducted. Besides drawing inference for covariate effects that are easy to interpret, prediction and model selection under the proposed Bayesian modal regression framework are also considered. Evidence from these analyses suggest 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. Computer programs for implementing the proposed Bayesian modal regression are available at https://github.com/rh8liuqy/Bayesian_modal_regression.