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

查看全文 作　　者：SHEN [1]Junshan;LI [1]Zhaonan;YU [1]Hanjun;FANG [1]Xiangzhong

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

出　　处：《Journal of Systems Science & Complexity》索引2017年第30卷第5期,共17页

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：加速的失败时间模型 Dirichlet 过程 errors-in-covariates 吉布斯采样可变选择

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

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