Bayesian Prediction Bounds from a Family of Exponentiated Distributions in the Presence of Outliers

In this paper, Bayesian prediction bounds for order statistics of future observations from a family of exponentiated distributions are obtained in the presence of a single outlier arising from different members of the same family of distributions. During an experimentation, we come across circumstances where one or more observations may not be homogeneous to rest of the observations and hence can be treated as outliers. Nowadays, the classification for outlier prediction are applied in various fields like bioinformatics, natural language processing, military application, geographical domains etc. We consider single outliers of two types in future observations when the sample size of the future sample is a random variable. The exponentiated exponential distribution has been used as a special case from the suggested family. We introduce numerical examples and compute Bayesian prediction bounds based on the real data, by using Markov chain Monte Carlo (MCMC) algorithm. © 2021 Taylor & Francis Group, LLC.