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.

Keywords:

Bayes estimation , EM algorithm , Kumaraswamy distribution , Maximum likelihood method , Progressive first-failure censoring