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Journal of Systems Science and Complexity

, Volume 30, Issue 5, pp 1189–1205 | Cite as

Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data

  • Junshan ShenEmail author
  • Zhaonan Li
  • Hanjun Yu
  • Xiangzhong Fang
Article
  • 82 Downloads

Abstract

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.

Keywords

Accelerated failure time model Dirichlet process errors-in-covariates Gibbs sampling variable selection 

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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Junshan Shen
    • 1
    Email author
  • Zhaonan Li
    • 1
  • Hanjun Yu
    • 1
  • Xiangzhong Fang
    • 1
  1. 1.School of Mathematical SciencesPeking UniversityBeijingChina

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