Predicting future order statistics with random sample size

被引：7

作者：

Barakat, Haroon ^{[1
]}

Khaled, Osama ^{[2
]}

Ghonem, Hadeer ^{[2
]}

机构：

[1] Zagazig Univ, Dept Math, Fac Sci, Zagazig, Egypt

[2] Port Said Univ, Dept Math & Comp Sci, Fac Sci, Port Said, Egypt

来源：

AIMS MATHEMATICS | 2021年 / 6卷 / 05期

关键词：

order statistics; characterization of distributions; point D-predictor; random sample size; COVID-19; EXPONENTIAL-DISTRIBUTION; INTERVALS; RECORD; CONTRACTION; TRANSLATION; DILATION;

D O I：

10.3934/math.2021304

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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).

引用

页码：5133 / 5147

页数：15

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