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Bayesian inference under progressive type-I interval censoring

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  • Yu-Jau Lin
  • Y. L. Lio

Abstract

Bayesian estimation for population parameter under progressive type-I interval censoring is studied via Markov Chain Monte Carlo (MCMC) simulation. Two competitive statistical models, generalized exponential and Weibull distributions for modeling a real data set containing 112 patients with plasma cell myeloma, are studied for illustration. In model selection, a novel Bayesian procedure which involves a mixture model is proposed. Then the mix proportion is estimated through MCMC and used as the model selection criterion.

Suggested Citation

  • Yu-Jau Lin & Y. L. Lio, 2012. "Bayesian inference under progressive type-I interval censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1811-1824, April.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1811-1824
    DOI: 10.1080/02664763.2012.683170
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    References listed on IDEAS

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    1. C. D. Kemp & Adrienne W. Kemp, 1987. "Rapid Generation of Frequency Tables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 277-282, November.
    2. Kundu, Debasis & Gupta, Rameshwar D., 2008. "Generalized exponential distribution: Bayesian estimations," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1873-1883, January.
    3. Chen, D.G. & Lio, Y.L., 2010. "Parameter estimations for generalized exponential distribution under progressive type-I interval censoring," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1581-1591, June.
    4. Gupta, Rameshwar D. & Kundu, Debasis, 2003. "Discriminating between Weibull and generalized exponential distributions," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 179-196, June.
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    Citations

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    Cited by:

    1. Sonal Budhiraja & Biswabrata Pradhan, 2020. "Point and interval estimation under progressive type-I interval censoring with random removal," Statistical Papers, Springer, vol. 61(1), pages 445-477, February.
    2. Rui Hua & Wenhao Gui, 2022. "Inference for copula-based dependent competing risks model with step-stress accelerated life test under generalized progressive hybrid censoring," Computational Statistics, Springer, vol. 37(5), pages 2399-2436, November.
    3. Sukhdev Singh & Yogesh Mani Tripathi, 2018. "Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring," Statistical Papers, Springer, vol. 59(1), pages 21-56, March.
    4. Jia, Xiang & Wang, Dong & Jiang, Ping & Guo, Bo, 2016. "Inference on the reliability of Weibull distribution with multiply Type-I censored data," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 171-181.
    5. Xun Xiao & Amitava Mukherjee & Min Xie, 2016. "Estimation procedures for grouped data – a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2110-2130, August.
    6. Refah Alotaibi & Hoda Rezk & Sanku Dey & Hassan Okasha, 2021. "Bayesian estimation for Dagum distribution based on progressive type I interval censoring," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.

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