Gibbs Sampler Algorithm of Bayesian Weighted Composite Quantile Regression
TIAN Yuzhu; WANG Liyong; WU Xinqian; TIAN Maozai
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
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.
The project was partly supported by the China Postdoctoral Science Foundation (Grant No. 2017M610156), the National Natural Science Foundation of China (Grant No. 11501167) and the Young Academic Leaders Project of Henan University of Science and Technology (Grant No. 13490008).
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TIAN Yuzhu; WANG Liyong; WU Xinqian; TIAN Maozai. Gibbs Sampler Algorithm of Bayesian Weighted Composite Quantile Regression. CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST, 2019, 35(2): 178-192.
