Bayesian nonparametric quantile regression using splines

被引：36

作者：

Thompson, Paul ^{[1
]}

Cai, Yuzhi ^{[1
]}

Moyeed, Rana ^{[1
]}

Reeve, Dominic ^{[2
]}

Stander, Julian ^{[1
]}

机构：

[1] Univ Plymouth, Sch Comp & Math, Plymouth PL4 8AA, Devon, England

[2] Univ Plymouth, Sch Engn, C CoDE, Plymouth PL4 8AA, Devon, England

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2010年 / 54卷 / 04期

基金：

英国工程与自然科学研究理事会;

关键词：

CHAIN MONTE-CARLO; ALGORITHM;

D O I：

10.1016/j.csda.2009.09.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the Metropolis-Hastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：1138 / 1150

页数：13

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