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Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction

Author

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  • R Alshenawy
  • Hanan Haj Ahmad
  • Ali Al-Alwan

Abstract

In this paper two prediction methods are used to predict the non-observed (censored) units under progressive Type-II censored samples. The lifetimes of the units follow Marshall-Olkin Pareto distribution. We observe the posterior predictive density of the non-observed units and construct predictive intervals as well. Furthermore, we provide inference on the unknown parameters of the Marshall-Olkin model, so we observe point and interval estimation by using maximum likelihood and Bayesian estimation methods. Bayes estimation methods are obtained under quadratic loss function. EM algorithm is used to obtain numerical values of the Maximum likelihood method and Gibbs and the Monte Carlo Markov chain techniques are utilized for Bayesian calculations. A simulation study is performed to evaluate the performance of the estimators with respect to the mean square errors and the biases. Finally, we find the best prediction method by implementing a real data example under progressive Type-II censoring schemes.

Suggested Citation

  • R Alshenawy & Hanan Haj Ahmad & Ali Al-Alwan, 2022. "Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-20, July.
    • Handle: RePEc:plo:pone00:0270750
    DOI: 10.1371/journal.pone.0270750
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    References listed on IDEAS

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    1. N. Balakrishnan, 2007. "Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 211-259, August.
    2. Biswabrata Pradhan & Debasis Kundu, 2009. "On progressively censored generalized exponential distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 497-515, November.
    3. N. Balakrishnan, 2007. "Rejoinder on: Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 290-296, August.
    4. Hanan Haj Ahmad & Niansheng Tang, 2021. "Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications," Journal of Mathematics, Hindawi, vol. 2021, pages 1-11, July.
    5. M. E. Ghitany & E. K. Al-Hussaini & R. A. Al-Jarallah, 2005. "Marshall-Olkin extended weibull distribution and its application to censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(10), pages 1025-1034.
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