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 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).

Automated summary

This paper proposes an efficient point predictor method based on the distribution properties of order statistics, which is evaluated and compared through simulation studies and real survival dataset applications. One example predicts the cumulative new cases per million for infection of the new Coronavirus (COVID-19).