Predicting future order statistics with random sample size

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 t...

Full description

Saved in:
Bibliographic Details
Published inAIMS mathematics Vol. 6; no. 5; pp. 5133 - 5147
Main Authors Barakat, Haroon, Khaled, Osama, Ghonem, Hadeer
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2021
Subjects
characterization of distributions
covid-19
order statistics
point d-predictor
random sample size
Online AccessGet full text

Cover

More Information
Summary: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).
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2021304