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Statistical Inference for the Gompertz Distribution Based on Adaptive Type-II Progressive Censoring Scheme

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  • M. M. Amein
  • M. El-Saady
  • M. M. Shrahili
  • A. R. Shafay
  • Sanku Dey

Abstract

The topic of estimating the parameters of Gompertz distribution using an adaptive Type-II progressively censored data are described in this paper. The unknown parameters, the reliability, and the hazard functions are estimated using maximum likelihood and Bayesian estimation methods. The approximate confidence intervals of them are then determined. Furthermore, the Markov chain Monte Carlo approach is used to perform a Bayesian estimate procedure and compute the credible intervals. Finally, a Monte Carlo simulation study is done to assess the performance of the two estimating methods, and a numerical example with real data is shown to demonstrate the procedures’ utility.

Suggested Citation

  • M. M. Amein & M. El-Saady & M. M. Shrahili & A. R. Shafay & Sanku Dey, 2022. "Statistical Inference for the Gompertz Distribution Based on Adaptive Type-II Progressive Censoring Scheme," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, July.
    • Handle: RePEc:hin:jnlmpe:1266384
    DOI: 10.1155/2022/1266384
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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.

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