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 expectationmaximization 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.