  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.
