Abstract
In this paper, we consider a new competitive model, the Marshall-Olkin extended Topp-Leone (MOETL) distribution and present its application to progressively first-failure censored data. The resulting new distribution includes the original distribution as a special case. This type of censoring contains as special cases various types of censoring schemes used in the literature. We compute the maximum likelihood estimations (MLEs) and the confidence intervals for the two unknown parameters based on the observed Fisher information matrix using asymptotic distribution of the maximum likelihood estimator. In addition, we propose to apply Markov Chain Monte Carlo (MCMC) method to tackle this problem, which allows us to construct the credible intervals. A numerical example based on real data is presented to illustrate the implementation of the proposed procedure. Finally, Monte Carlo simulations are performed to observe the behavior of the proposed methods.