Bayesian bent line quantile regression model

Abstract

This article introduces a Bayesian estimating method for a bent line quantile regression model. Within the Bayesian framework, regression coefficients and threshold can be simultaneously estimated, addressing the problem of optimizing the loss function in frequentist approaches, while the statistical inference on the threshold is direct. Simulation studies and two real data examples show that the Bayesian method demonstrates better sample performance.