Simultaneous fitting of Bayesian penalised quantile splines

作     者：Rodrigues, T. Dortet-Bernadet, J. -L. Fan, Y. 

作者机构：Univ New South Wales Sch Math & Stat Sydney NSW 2052 Australia Univ Brasilia Dept Estat BR-70910900 Brasilia DF Brazil Univ Strasbourg CIIRS UMR 7501 Inst Rech Math Avancee Strasbourg France 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (计算统计学与数据分析)

年 卷 期：2019年第134卷

页      面：93-109页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CAPES Foundation via the Science Without Borders [BEX 0979/13-9] ARC ACEMS, Australia 

主　　题：Bayesian quantile pyramid Simultaneous quantile regression B-splines O'Sullivan penalised splines Nonparametric quantile regression 

摘      要：Bayesian simultaneous estimation of nonparametric quantile curves is a challenging problem, requiring a flexible and robust data model whilst satisfying the monotonicity or noncrossing constraints on the quantiles. The pyramid quantile regression method for simultaneous linear quantile fitting is adapted for the spline regression setting. In high dimensional problems, the choice of the pyramid locations becomes crucial for a robust parameter estimation. The optimal pyramid locations are derived which then allows for an efficient adaptive block-update MCMC scheme to be proposed for posterior computation. Simulation studies show that the proposed method provides estimates with significantly smaller errors and better empirical coverage probability when compared to existing alternative approaches. The method is illustrated with three real applications. (C) 2018 Elsevier B.V. All rights reserved.