Regression Adjustment for Noncrossing Bayesian Quantile Regression. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Rodrigues, T.; Fan, Y.
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 2(2017)
• Page Start: 275
• Page End: 284
• Publication Date: 2017-04-03
• Subjects: Asymmetric Laplace distribution -- Crossing quantile regression -- Gaussian process regression -- Monotonicity; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1172016
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: A two-stage approach is proposed to overcome the problem in quantile regression, where separately fitted curves for several quantiles may cross. The standard Bayesian quantile regression model is applied in the first stage, followed by a Gaussian process regression adjustment, which monotonizes the quantile function while borrowing strength from nearby quantiles. The two-stage approach is computationally efficient, and more general than existing techniques. The method is shown to be competitive with alternative approaches via its performance in simulated examples. Supplementary materials for the article are available online.