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

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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.

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Authors and Affiliations

  1. School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Junshan Shen, Zhaonan Li, Hanjun Yu & Xiangzhong Fang

Authors
  1. Junshan Shen
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  2. Zhaonan Li
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  3. Hanjun Yu
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  4. Xiangzhong Fang
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Corresponding author

Correspondence to Junshan Shen.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 11171007/A011103, 11171230, and 11471024.

This paper was recommended for publication by Editor LI Qizhai.

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Shen, J., Li, Z., Yu, H. et al. Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data. J Syst Sci Complex 30, 1189–1205 (2017). https://doi.org/10.1007/s11424-017-6010-2

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  • DOI: https://doi.org/10.1007/s11424-017-6010-2

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