| ��Ҷ˹���Ϸ�λ�ع��Gibbs�����㷨 |
| |
| 引用本文： | ������,������,����Ǭ,��ï��. ��Ҷ˹���Ϸ�λ�ع��Gibbs�����㷨[J]. 应用概率统计, 2019, 35(2): 178-192. DOI: 10.3969/j.issn.1001-4268.2019.02.006 |
| |
| 作者姓名： | ������ ������ ����Ǭ ��ï�� |
| |
| 作者单位： | ???????????????????, ????, 100081; ???????????????????, ????, 471023; ?й????????????????о?????, ????, 100872 |
| |
| 摘 要： |
|
| 关 键 词： | ????????Laplace???(CALD) ???????????????(MCMC)?? ??λ??? Gibbs???? ?????? ??????? |
Gibbs Sampler Algorithm of Bayesian Weighted Composite Quantile Regression |
| |
| TIAN Yuzhu,WANG Liyong,WU Xinqian,TIAN Maozai. Gibbs Sampler Algorithm of Bayesian Weighted Composite Quantile Regression[J]. Chinese Journal of Applied Probability and Statisties, 2019, 35(2): 178-192. DOI: 10.3969/j.issn.1001-4268.2019.02.006 |
| |
| Authors: | TIAN Yuzhu WANG Liyong WU Xinqian TIAN Maozai |
| |
| Affiliation: | School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471023, China; Center for Applied Statistics of Renmin;University of China, Beijing, 100872, China |
| |
| Abstract: | Most regression modeling is based on traditional mean regression which results in non-robust estimation results for non-normal errors. Compared to conventional mean regression, composite quantile regression (CQR) may produce more robust parameters estimation. Based on a composite asymmetric Laplace distribution (CALD), we build a Bayesian hierarchical model for the weighted CQR (WCQR). The Gibbs sampler algorithm of Bayesian WCQR is developed to implement posterior inference. Finally, the proposed method are illustrated by some simulation studies and a real data analysis. |
| |
| Keywords: | CALD Markov chain Monte Carlo (MCMC) algorithm quantile regression Gibbs sampler hierarchical model posterior inference |
|
| 点击此处可从《应用概率统计》浏览原始摘要信息 |
|
点击此处可从《应用概率统计》下载全文 |
|
