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On generalized progressive hybrid censoring in presence of competing risks

Author

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  • Arnab Koley

    (Indian Institute of Technology Kanpur)

  • Debasis Kundu

    (Indian Institute of Technology Kanpur)

Abstract

The progressive Type-II hybrid censoring scheme introduced by Kundu and Joarder (Comput Stat Data Anal 50:2509–2528, 2006), has received some attention in the last few years. One major drawback of this censoring scheme is that very few observations (even no observation at all) may be observed at the end of the experiment. To overcome this problem, Cho et al. (Stat Methodol 23:18–34, 2015) recently introduced generalized progressive censoring which ensures to get a pre specified number of failures. In this paper we analyze generalized progressive censored data in presence of competing risks. For brevity we have considered only two competing causes of failures, and it is assumed that the lifetime of the competing causes follow one parameter exponential distributions with different scale parameters. We obtain the maximum likelihood estimators of the unknown parameters and also provide their exact distributions. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals can be obtained. Asymptotic and bootstrap confidence intervals are also provided for comparison purposes. We further consider the Bayesian analysis of the unknown parameters under a very flexible beta–gamma prior. We provide the Bayes estimates and the associated credible intervals of the unknown parameters based on the above priors. We present extensive simulation results to see the effectiveness of the proposed method and finally one real data set is analyzed for illustrative purpose.

Suggested Citation

  • Arnab Koley & Debasis Kundu, 2017. "On generalized progressive hybrid censoring in presence of competing risks," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(4), pages 401-426, May.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:4:d:10.1007_s00184-017-0611-6
    DOI: 10.1007/s00184-017-0611-6
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    References listed on IDEAS

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    1. Ping Chan & Hon Ng & Feng Su, 2015. "Exact likelihood inference for the two-parameter exponential distribution under Type-II progressively hybrid censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(6), pages 747-770, August.
    2. N. Balakrishnan & Qihao Xie & D. Kundu, 2009. "Exact inference for a simple step-stress model from the exponential distribution under time constraint," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 251-274, March.
    3. Balakrishnan, N. & Childs, A. & Chandrasekar, B., 2002. "An efficient computational method for moments of order statistics under progressive censoring," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 359-365, December.
    4. Debasis Kundu & Rameshwar Gupta, 2007. "Analysis of Hybrid Life-tests in Presence of Competing Risks," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 159-170, February.
    5. Kundu, Debasis & Joarder, Avijit, 2006. "Analysis of Type-II progressively hybrid censored data," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2509-2528, June.
    6. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
    7. A. Childs & B. Chandrasekar & N. Balakrishnan & D. Kundu, 2003. "Exact likelihood inference based on Type-I and Type-II hybrid censored samples from the exponential distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 319-330, June.
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    Cited by:

    1. Kotb Mohammed S., 2018. "Bayesian Prediction Bounds for the Exponential-type Distribution Based on Generalized Progressive Hybrid Censoring Scheme," Stochastics and Quality Control, De Gruyter, vol. 33(2), pages 93-101, December.
    2. Prakash Chandra & Yogesh Mani Tripathi & Liang Wang & Chandrakant Lodhi, 2023. "Estimation for Kies distribution with generalized progressive hybrid censoring under partially observed competing risks model," Journal of Risk and Reliability, , vol. 237(6), pages 1048-1072, December.
    3. Abd El-Raheem M. Abd El-Raheem & Mona Hosny & Mahmoud H. Abu-Moussa, 2021. "On Progressive Censored Competing Risks Data: Real Data Application and Simulation Study," Mathematics, MDPI, vol. 9(15), pages 1-17, July.
    4. Alaa H. Abdel-Hamid & Atef F. Hashem, 2021. "Inference for the Exponential Distribution under Generalized Progressively Hybrid Censored Data from Partially Accelerated Life Tests with a Time Transformation Function," Mathematics, MDPI, vol. 9(13), pages 1-28, June.
    5. Wang, Liang & Tripathi, Yogesh Mani & Lodhi, Chandrakant & Zuo, Xuanjia, 2022. "Inference for constant-stress Weibull competing risks model under generalized progressive hybrid censoring," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 70-83.
    6. Arnab Koley & Debasis Kundu, 2021. "Analysis of progressive Type‐II censoring in presence of competing risk data under step stress modeling," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 115-136, May.

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