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Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data

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  • Ahmed Elshahhat

    (Zagazig University)

  • Mazen Nassar

    (King Abdulaziz University)

Abstract

Adaptive Type-II progressive hybrid censoring scheme has been proposed to increase the efficiency of statistical analysis and save the total test time on a life-testing experiment. This article deals with the problem of estimating the parameters, survival and hazard rate functions of the two-parameter Hjorth distribution under adaptive Type-II progressive hybrid censoring scheme using maximum likelihood and Bayesian approaches. The two-sided approximate confidence intervals of the unknown quantities are constructed. Under the assumption of independent gamma priors, the Bayes estimators are obtained using squared error loss function. Since the Bayes estimators cannot be expressed in closed forms, Lindley’s approximation and Markov chain Monte Carlo methods are considered and the highest posterior density credible intervals are also obtained. To study the behavior of the various estimators, a Monte Carlo simulation study is performed. The performances of the different estimators have been compared on the basis of their average root mean squared error and relative absolute bias. Finally, to show the applicability of the proposed estimators a data set of industrial devices has been analyzed.

Suggested Citation

  • Ahmed Elshahhat & Mazen Nassar, 2021. "Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data," Computational Statistics, Springer, vol. 36(3), pages 1965-1990, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01065-8
    DOI: 10.1007/s00180-021-01065-8
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    Cited by:

    1. Subhankar Dutta & Sanku Dey & Suchandan Kayal, 2024. "Bayesian survival analysis of logistic exponential distribution for adaptive progressive Type-II censored data," Computational Statistics, Springer, vol. 39(4), pages 2109-2155, June.
    2. Sanku Dey & Ahmed Elshahhat & Mazen Nassar, 2023. "Analysis of progressive type-II censored gamma distribution," Computational Statistics, Springer, vol. 38(1), pages 481-508, March.
    3. Hanan Haj Ahmad & Ahmed Elshahhat, 2025. "A New Hjorth Distribution in Its Discrete Version," Mathematics, MDPI, vol. 13(5), pages 1-25, March.
    4. Ahmed Elshahhat & Mazen Nassar, 2024. "Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models," Statistical Papers, Springer, vol. 65(3), pages 1163-1196, May.
    5. Ahmed Elshahhat & Refah Alotaibi & Mazen Nassar, 2022. "Inferences for Nadarajah–Haghighi Parameters via Type-II Adaptive Progressive Hybrid Censoring with Applications," Mathematics, MDPI, vol. 10(20), pages 1-19, October.
    6. Refah Alotaibi & Mazen Nassar & Ahmed Elshahhat, 2022. "Computational Analysis of XLindley Parameters Using Adaptive Type-II Progressive Hybrid Censoring with Applications in Chemical Engineering," Mathematics, MDPI, vol. 10(18), pages 1-24, September.
    7. Kyeongjun Lee, 2024. "Inference for Parameters of Exponential Distribution under Combined Type II Progressive Hybrid Censoring Scheme," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    8. Refah Alotaibi & Mazen Nassar & Hoda Rezk & Ahmed Elshahhat, 2022. "Inferences and Engineering Applications of Alpha Power Weibull Distribution Using Progressive Type-II Censoring," Mathematics, MDPI, vol. 10(16), pages 1-21, August.
    9. Ahmed Elshahhat & Osama E. Abo-Kasem & Heba S. Mohammed, 2023. "Survival Analysis of the PRC Model from Adaptive Progressively Hybrid Type-II Censoring and Its Engineering Applications," Mathematics, MDPI, vol. 11(14), pages 1-26, July.

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