Bayesian quantile semiparametric mixed-effects double regression models  

作　　者：Duo Zhang Liucang Wu Keying Ye Min Wang 

机构地区：[1]Department of Mathematical Sciences,Michigan Technological University,Houghton,MI,USA [2]Faculty of Science,Kunming University of Science and Technology,Kunming,People's Republic of China [3]Department of Management Science and Statistics,The University of Texas at San Antonio,San Antonio,TX,ÜSA

出　　处：《Statistical Theory and Related Fields》2021年第4期303-315,共13页统计理论及其应用（英文）

基　　金：Dr.Wu was supported by the National Natural Science Foundation of China under grant 11861041;Drs.Keying Ye and Min Wang were partially supported by a grant from the UTSA Vice President for Research,Economic Development,and Knowledge Enterprise at the University of Texas at San Antonio.

摘　　要：Semiparametric mixed-effects double regression models have been used for analysis of longitu-dinal data in a variety of applications,as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors.However,these models are commonly estimated based on the normality assumption for the errors and the results may thus be sensitive to outliers and/or heavy-tailed data.Quantile regression is an ideal alternative to deal with these problems,as it is insensitive to heteroscedasticity and outliers and can make statistical analysis more robust.In this paper,we consider Bayesian quantile regression analysis for semiparamet-ric mixed-effects double regression models based on the asymmetric Laplace distribution for the errors.We construct a Bayesian hierarchical model and then develop an efficient Markov chain Monte Carlo sampling algorithm to generate posterior samples from the full posterior dis-tributions to conduct the posterior inference.The performance of the proposed procedure is evaluated through simulation studies and a real data application.

关 键 词：B-SPLINE MCMC methods quantile regression semiparametric mixed-effects double regression model 

分 类 号：O17[理学—数学]