Bayesian One Sample Prediction of Future GOS’s From A Class of Finite Mixture Distributions Based On Generalized Type-I Hybrid Censoring Scheme
Abstract
In this paper, the Bayesian prediction intervals for a future gos's from a mixture of two components from a class of continuous distributions under generalized Type-I hybrid censoring scheme are computed. We consider the one sample prediction technique. A mixture of two Weibull components model
is given as an application. Our results are specialized to upper order statistics and upper record values. The results obtained by using the Markov Chain Monte Carlo (MCMC) algorithm.
Keywords: Generalized order statistics; Bayesian prediction; One-sample scheme; Finite mixtures; Generalized Type-I hybrid censoring scheme; MCMC algorithm.
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