Prediction intervals based on Gompertz doubly censored data

This article is concerned with the problem of deriving Bayesian prediction bounds for the Gompertz \(Gomp(\alpha ,\,\beta )]\) distribution. Based on doubly Type II censored data, Bayesian prediction bounds for both the future observations will be derived. Two different sampling schemes have been considered. A conjugate prior for the one parameter case, as well as a joint prior for the two parameters case are outlined. Some numerical examples are given to illustrate the results.