reliability analysis of kumaraswamy distribution under progressive first-failure censoring

In this article‎, ‎we consider the estimation of the parameters and reliability characteristics of kumaraswamy distribution using progressive first failure censored samples‎. ‎first‎, ‎we derive the maximum likelihood estimates using an expectation-maximization algorithm and compute the observed information of the parameters that can be used for constructing asymptotic confidence intervals‎. ‎we also compute the bayes estimates of the parameters using lindley approximation as well as the metropolis-hastings algorithm‎. ‎furthermore‎, ‎we derive the highest posterior density credible intervals‎. ‎simulation studies are conducted to evaluate the performance of the point and interval estimators‎. ‎finally‎, ‎two examples of real data sets are provided to illustrate the proposed procedures.