Applied Mathematics & Information Sciences

Author Country (or Countries)

Saudi Arabia


In this article, we intend to study the progressive Type-I censoring (PT-TC) that has been examined, employing the Marshall-Olkin extended exponential (MOEE) distribution as the fundamental lifetime distribution. The censoring technique is believed to be independent and non-informative. Because maximum likelihood (ML) estimators cannot be derived in closed form, ML estimates (MLEs) are calculated via Newton-Raphson method approaches. In this approach, MLEs and asymptotic confidence intervals for unknown parameters are produced. Under squared error and linear exponential (LINEX) loss functions, the Bayes estimations of unknown parameters with gamma priors are evaluated. Once both parameters are unknown, the Bayes estimators cannot be computed explicitly. Then, the Markov Chain Monte Carlo (MCMC) technique is employed to construct Bayes estimates using the Metropolis-Hasting (MH) algorithm. The highest posterior density (HPD) credible intervals of the unknown parameter are calculated. Simulation studies are carried out to explore the finite sample effectiveness of the recommended estimators, as well as data set analyses at various schemes of PT-TC samples.

Digital Object Identifier (DOI)