Shrinkage estimation of fixed and random effects in linear quantile mixed models

被引:0
|
作者
Ji, Yonggang [1 ]
Shi, Haifang [1 ]
机构
[1] Civil Aviat Univ China, Sch Sci, Tianjin, Peoples R China
关键词
Quantile mixed regression; Cholesky decomposition; expectile mixed regression; partially collapsed Gibbs sampling; Metropolis-Hastings acceptance-rejection; RANDOM EFFECTS SELECTION; VARIABLE SELECTION; REGRESSION-MODEL; ECONOMIC-MODEL; PANEL-DATA; AVERAGE; PRIORS; CRIME;
D O I
10.1080/02664763.2021.1962262
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
This paper presents a Bayesian analysis of linear mixed models for quantile regression using a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We consider several novel Bayesian shrinkage approaches for both fixed and random effects in a linear mixed quantile model using extended L-1 penalties. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. We also extend the framework to a Bayesian mixed expectile model and develop a Metropolis- Hastings acceptance-rejection (MHAR) algorithm using proposal densities based on iteratively weighted least squares estimation. The proposed approach is then illustrated via both simulated and real data examples. Results indicate that the proposed approach performs very well in comparison to the other approaches.
引用
收藏
页码:3693 / 3716
页数:24
相关论文
共 50 条