Interval Prediction of Future Order Statistics in Two-Component Mixture Inverse Lomax Model: A Bayesian Approach

Abstract

SYNOPTIC ABSTRACTThe inverse Lomax distribution is studied as a probability distribution following a time-to-failure system. This article is based on the study of Bayesian prediction intervals for certain future ordered observations in samples from the two-component inverse Lomax mixture distribution. Specifically, the lower and upper prediction bounds are calculated for five-number summary, i.e., minimum, first quartile, median, third quartile, and maximum observation in the future sample of specified size. The goal is achieved by the application of the posterior predictive distribution and Type-I right censoring scheme. Real-data application is provided for the illustration of algebraic results.