Bayesian variable selection in binary quantile regression

被引：7
|
作者：
Oh, Man-Suk [1 ]
Park, Eun Sug [2 ]
So, Beong-Soo [1 ]
机构：
[1] Ewha Womans Univ, Dept Stat, Seoul 120750, South Korea
[2] Texas A&M Transportat Inst, 3135 TAMU, College Stn, TX 77843 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2016年 / 118卷
基金：
新加坡国家研究基金会;
关键词：
Bayesian model selection; Bayes factor; Quantile regression; Markov chain Monte Carlo; WEIGHTED LIKELIHOOD RATIO; DISTRIBUTIONS; HYPOTHESES; MODEL;
D O I：
10.1016/j.spl.2016.07.001
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We propose a simple Bayesian variable selection method in binary quantile regression. Our method computes the Bayes factors of all candidate models simultaneously based on a single set of MCMC samples from a model that encompasses all candidate models. The method deals with multicollinearity problems and variable selection under constraints. (C) 2016 Published by Elsevier B.V.
引用
收藏
页码：177 / 181
页数：5
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