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Bayesian multiple change-points estimation for hazard with censored survival data from exponential distributions

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Abstract

Change-point models are generative models in which the underlying generative parameters change at different points in time. A Bayesian approach to the problem of hazard change with unknown multiple change-points is developed using informative priors for censored survival data. For the exponential distribution, piecewise constant hazard is considered with change-point estimation. The stochastic approximation Monte Carlo algorithm is implemented for efficient calculation of the posterior distributions. The performance of the proposed estimator is checked via simulation. As a real data application, Leukemia data are analyzed by the proposed method and compared with other previous non-Bayesian method.

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Acknowledgements

This research was supported by Mid-career Science Research Program through the National Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2018R1A2B6001664). It was also supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (no. 20161210200610) and by Korea Electric Power Corporation (Grant number: R18XA01).

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Authors and Affiliations

  1. Department of Statistics, Duksung Women’s University, Seoul, 01369, South Korea

    Jaehee Kim

  2. Department of Applied Statistics, Korea University, 2511, Sejong-ro, Sejong City, 339-700, South Korea

    Sooyoung Cheon

  3. Department of Biostatistics, Columbia University, New York, NY, USA

    Zhezhen Jin

Authors
  1. Jaehee Kim
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  2. Sooyoung Cheon
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  3. Zhezhen Jin
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Correspondence to Jaehee Kim.

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Kim, J., Cheon, S. & Jin, Z. Bayesian multiple change-points estimation for hazard with censored survival data from exponential distributions. J. Korean Stat. Soc. 49, 15–31 (2020). https://doi.org/10.1007/s42952-019-00016-w

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  • DOI: https://doi.org/10.1007/s42952-019-00016-w

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