摘要
大多数基于传统均值回归的建模方法都对非正态误差表现出不稳健的估计结果.和传统均值回归相比,复合分位回归(CQR)可以产生稳健的估计.基于一个复合反对称Laplace分布(CALD),我们建立了加权复合分位回归(WCQR)的贝叶斯分层模型.Gibbs抽样算法被发展用于WCQR的后验推断.最后,我们提供了一些模拟研究和一个实际数据分析来验证所提方法.
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.
作者
田玉柱
王立勇
武新乾
田茂再
TIAN Yuzhu;WANG Liyong;WU Xinqian;TIAN Maozai(School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, 100081, China;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)
出处
《应用概率统计》
CSCD
北大核心
2019年第2期178-192,共15页
Chinese Journal of Applied Probability and Statistics
基金
partly supported by the China Postdoctoral Science Foundation(Grant No.2017M610156)
the National Natural Science Foundation of China(Grant No.11501167)
the Young Academic Leaders Project of Henan University of Science and Technology(Grant No.13490008)
