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AIMS Mathematics

2021, Volume 6, Issue 5: 5133-5147. doi: 10.3934/math.2021304
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Research article

Predicting future order statistics with random sample size

  • 1.
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
  • 2.
    Department of Mathematics and Computer Science, Faculty of Science, Port-Said University, Port-Said, Egypt
  • Received: 23 December 2020 Accepted: 03 March 2021 Published: 10 March 2021

  • MSC : 62G30, 62E15

  • We suggest a new method for constructing an efficient point predictor for the future order statistics when the sample size is a random variable. The suggested point predictor is based on some characterization properties of the distributions of order statistics. For several distributions, including the mixture distribution, the performance of the suggested predictor is evaluated by means of a comprehensive simulation study. Three examples of real lifetime data-sets are analyzed by using this method and compared with an efficient recent method given by Barakat et al. [1], that deals with non-random sample sizes. One of these examples predicts the accumulative new cases per million for infection of the new Coronavirus (COVID-19).

    Citation: Haroon Barakat, Osama Khaled, Hadeer Ghonem. Predicting future order statistics with random sample size[J]. AIMS Mathematics, 2021, 6(5): 5133-5147. doi: 10.3934/math.2021304

    Related Papers:

  • We suggest a new method for constructing an efficient point predictor for the future order statistics when the sample size is a random variable. The suggested point predictor is based on some characterization properties of the distributions of order statistics. For several distributions, including the mixture distribution, the performance of the suggested predictor is evaluated by means of a comprehensive simulation study. Three examples of real lifetime data-sets are analyzed by using this method and compared with an efficient recent method given by Barakat et al. [1], that deals with non-random sample sizes. One of these examples predicts the accumulative new cases per million for infection of the new Coronavirus (COVID-19).


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  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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