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Information Sciences Letters

Information Sciences Letters

Abstract

In this article, we introduce a simple algorithm to generating a new type-II progressive censoring scheme for two samples. It is observed that the proposed algorithm can be applied for any continues probability distribution. Moreover, the description model and necessary assumptions are discussed. In addition, the steps of simple generation algorithm along with programming steps are also constructed on real example. The inference of two Weibull Frechet populations are discussed under the proposed algorithm. Both classical and Bayesian inferential approaches of the distribution parameters are discussed. Furthermore, approximate confidence intervals are constructed based on the asymptotic distribution of the maximum likelihood estimators. Also, we apply four methods of Bootstrap to construct confidence intervals. From Bayesian aspect, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique and credible intervals are also carried out. The Bayes inference based on the squared error and LINEX loss functions is obtained. Simulation results have been implemented to obtain the accuracy of the estimators. Finally, one real data set has been analyzed for illustrative purposes both the proposed algorithm and methods of estimation.

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