Regression Adjustment for Noncrossing Bayesian Quantile Regression

2015/02/04 by Yanan Fan, Rodrigues, Thais, Fan, Yanan · 1 citation
Computer Science · Decision Sciences · Mathematics · #62J99 #Advanced Statistical Process Monitoring #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1502.01115

openalex publication_date 2015/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 whilst 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.

Cited by

• Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models

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