Semiparametric Bayesian Inference for Accelerated Failure Time Models with Errors-in-Covariates and Doubly Censored Data  

作　　者：SHEN Junshan LI Zhaonan YU Hanjun FANG Xiangzhong 

机构地区：[1]School of Mathematical Sciences,Peking University,Beijing 100871,China

出　　处：《Journal of Systems Science & Complexity》2017年第5期1189-1205,共17页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.11171007/A011103,11171230,and 11471024

摘　　要：This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression errors via the Dirichlet processes. Moreover, the authors extend the Bayesian Lasso approach to our semiparametric model for variable selection. The authors develop the Markov chain Monte Carlo strategies for posterior calculation. Simulation studies are conducted to show the performance of the proposed method. The authors also demonstrate the implementation of the method using analysis of PBC data and ACTG 175 data.

关 键 词：Accelerated failure time model Dirichlet process errors-in-covariates Gibbs sampling variable selection 

分 类 号：O212[理学—概率论与数理统计]