Bayesian prediction for type-II progressive-censored data from the Rayleigh distribution under progressive-stress model

Abstract

The two-sample prediction is considered to derive Bayesian prediction bounds for future order statistics based on progressively type-II censored informative samples obtained from progressive-stress accelerated life testing models. The lifetime of an item under the use condition stress is assumed to follow the Rayleigh distribution with a scale parameter satisfying the inverse power law. The informative and future samples are assumed to be obtained from the same population. Explicit forms for prediction bounds of the first future order statistic are obtained in the case of one unknown parameter. When two parameters are unknown, a simulation study is performed and numerical computations are carried out, based on three different progressive-censoring schemes. The coverage probabilities and average interval lengths of the confidence intervals are computed via a Monte Carlo simulation.