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Inference of a competing risks model with partially observed failure causes under improved adaptive type-II progressive censoring

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  • Subhankar Dutta
  • Suchandan Kayal

Abstract

In this paper, a competing risks model is analyzed based on improved adaptive type-II progressive censored sample (IAT-II PCS). Two independent competing causes of failures are considered. It is assumed that lifetimes of the competing causes of failure follow exponential distributions with different means. Maximum likelihood estimators (MLEs) for the unknown model parameters are obtained. Using asymptotic normality property of MLE, the asymptotic confidence intervals are constructed. Existence and uniqueness properties of the MLEs are studied. Further, bootstrap confidence intervals are computed. The Bayes estimators are obtained under symmetric and asymmetric loss functions with non-informative and informative priors. For informative priors, independent gamma distributions are considered. Highest posterior density (HPD) credible intervals are obtained. A Monte Carlo simulation study is carried out to compare performance of the established estimates. Furthermore, three different optimality criteria are proposed to obtain the optimal censoring plan. Finally, a real-life data set is considered for illustrative purposes.

Suggested Citation

  • Subhankar Dutta & Suchandan Kayal, 2023. "Inference of a competing risks model with partially observed failure causes under improved adaptive type-II progressive censoring," Journal of Risk and Reliability, , vol. 237(4), pages 765-780, August.
    • Handle: RePEc:sae:risrel:v:237:y:2023:i:4:p:765-780
    DOI: 10.1177/1748006X221104555
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    References listed on IDEAS

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    1. Essam A. Ahmed & Ziyad Ali Alhussain & Mukhtar M. Salah & Hanan Haj Ahmed & M. S. Eliwa, 2020. "Inference of progressively type-II censored competing risks data from Chen distribution with an application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(13-15), pages 2492-2524, November.
    2. Pareek, Bhuvanesh & Kundu, Debasis & Kumar, Sumit, 2009. "On progressively censored competing risks data for Weibull distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4083-4094, October.
    3. 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.
    4. Cramer, Erhard & Schmiedt, Anja Bettina, 2011. "Progressively Type-II censored competing risks data from Lomax distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1285-1303, March.
    5. 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.
    6. Lin, Chien-Tai & Chou, Cheng-Chieh & Huang, Yen-Lung, 2012. "Inference for the Weibull distribution with progressive hybrid censoring," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 451-467.
    7. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
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