Bayesian bent line quantile regression model. Issue 17 (9th August 2021)

• Record Type: Journal Article
• Authors: Li, Yi; Hu, Zongyi
• Is Part Of: Communications in statistics. Volume 50:Issue 17(2021)
• Page Start: 3972
• Page End: 3987
• Publication Date: 2021-08-09
• Subjects: Bent line -- quantile regression -- Bayesian method; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1710750
• Languages: English
• ISSNs: 0361-0926

Abstract

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.