Bayesian Prediction of order statistics based on finite mixture of general class of distributions under Random Censoring

Abstract

This paper focuses on the Bayesian prediction of k^th ordered future observations modelled by a two-component mixture of general class of distributions. Samples under consideration are subject to random censoring. A closed form of Bayesian predictive density is obtained under a two-sample scheme. Applications to Weibull and Burr XII components are presented and comparisons with previous results are made. A numerical example is presented for special cases of the exponential and Lomax components to obtain interval prediction of first and last order statistics.