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Inference for Weibull distribution under adaptive Type-I progressive hybrid censored competing risks data

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  • S. K. Ashour
  • M. Nassar

Abstract

In this paper, a competing risks model is considered under adaptive type-I progressive hybrid censoring scheme (AT-I PHCS). The lifetimes of the latent failure times have Weibull distributions with the same shape parameter. We investigate the maximum likelihood estimation of the parameters. Bayes estimates of the parameters are obtained based on squared error and LINEX loss functions under the assumption of independent gamma priors. We propose to apply Markov Chain Monte Carlo (MCMC) techniques to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. To evaluate the performance of the estimators, a simulation study is carried out.

Suggested Citation

  • S. K. Ashour & M. Nassar, 2017. "Inference for Weibull distribution under adaptive Type-I progressive hybrid censored competing risks data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(10), pages 4756-4773, May.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:10:p:4756-4773
    DOI: 10.1080/03610926.2015.1083111
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    Cited by:

    1. Muqrin A. Almuqrin & Mukhtar M. Salah & Essam A. Ahmed, 2022. "Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications," Mathematics, MDPI, vol. 10(22), pages 1-38, November.
    2. O. E. Abo-Kasem & Ehab M. Almetwally & Wael S. Abu El Azm, 2023. "Inferential Survival Analysis for Inverted NH Distribution Under Adaptive Progressive Hybrid Censoring with Application of Transformer Insulation," Annals of Data Science, Springer, vol. 10(5), pages 1237-1284, October.
    3. Mazen Nassar & Refah Alotaibi & Chunfang Zhang, 2022. "Estimation of Reliability Indices for Alpha Power Exponential Distribution Based on Progressively Censored Competing Risks Data," Mathematics, MDPI, vol. 10(13), pages 1-25, June.

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