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Bayesian and Non-Bayesian Estimation of the Nadaraj ah–Haghighi Distribution: Using Progressive Type-1 Censoring Scheme

Author

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  • Ibrahim Elbatal

    (Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Naif Alotaibi

    (Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Salem A. Alyami

    (Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Mohammed Elgarhy

    (The Higher Institute of Commercial Sciences, Al Mahalla Al Kubra 31951, Egypt)

  • Ahmed R. El-Saeed

    (Department of Basic Sciences, Obour High Institute for Management & Informatics, Obour 11848, Egypt)

Abstract

This work will address the problem of estimating the parameters for the Nadaraj ah–Haghighi (NH) distribution using progressive Type-1 censoring (PT1C) utilizing Bayesian and non-Bayesian approaches. To apply PT1C, censoring times for each stage of censoring needed to be known before the experiment started. To solve this issue of censoring time selection, qauntiles from the NH lifetime distribution will be used as PT1C censoring time points. Maximum likelihood (ML) estimators (MLEs) and asymptotic confidence intervals (ACoIs) are produced with a focus on the censoring technique. Bayes estimates (BEs) and accompanying maximum posterior density (PD) credible interval estimations are also created via the squared error (SEr) loss function. The BEs are evaluated using the Markov Chain Monte Carlo (MCMC) technique and the Metropolis–Hasting (MH) algorithm. An analysis of an actual data set demonstrates the theoretical implications of MLEs and BEs for defined schemes of PT1C samples. Finally, simulation results will be used to compare the performance of the various recommended estimators.

Suggested Citation

  • Ibrahim Elbatal & Naif Alotaibi & Salem A. Alyami & Mohammed Elgarhy & Ahmed R. El-Saeed, 2022. "Bayesian and Non-Bayesian Estimation of the Nadaraj ah–Haghighi Distribution: Using Progressive Type-1 Censoring Scheme," Mathematics, MDPI, vol. 10(5), pages 1-16, February.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:760-:d:759929
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    References listed on IDEAS

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    1. Devendra Kumar & Sanku Dey & Saralees Nadarajah, 2017. "Extended exponential distribution based on order statistics," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 9166-9184, September.
    2. Haghighi, Firoozeh, 2014. "Optimal design of accelerated life tests for an extension of the exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 251-256.
    3. N. Balakrishnan & Donghoon Han & G. Iliopoulos, 2011. "Exact inference for progressively Type-I censored exponential failure data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(3), pages 335-358, May.
    4. Sanku Dey & Chunfang Zhang & A. Asgharzadeh & M. Ghorbannezhad, 2017. "Comparisons of Methods of Estimation for the NH Distribution," Annals of Data Science, Springer, vol. 4(4), pages 441-455, December.
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    Cited by:

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