2012/02/27 by J.-L. Dortet-Bernadet, J. -L. Dortet-Bernadet, Yanan Fan +3
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.1202.5883
arxiv created 2012/02/27 · openalex publication_date 2012/02/27 · arxiv updated 2012/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quantile regression has received increased attention in the statistics community in recent years. This article adapts an auxiliary variable method, commonly used in Bayesian variable selection for mean regression models, to the fitting of quantile regression curves. We focus on the fitting of regression splines, with unknown number and location of knots. We provide an efficient algorithm with Metropolis-Hastings updates whose tuning is fully automated. The method is tested on simulated and real examples and its extension to additive models is described. Finally we propose a simple postprocessing procedure to deal with the problem of the crossing of multiple separately estimated quantile curves.