Bayesian Estimation of Inverted Exponentiated Weibull Distribution under Progressive Type II Censoring with Binomial Removal

In this paper, we conduct the experiment to estimate three-parameter of the inverted exponentiated Weibull (IEW) distribution. The prior distribution of the model parameters is the gamma distribution. The tests are carried out under progressive type II censoring with binomial removal. Maximum likelihood estimates (MLEs), Bayes estimates are obtained by Newton-Raphson algorithm and Bayes methods. Also, we take survival function and hazard function of the IEW model. Bayesian estimates are derived by the hybrid Markov chain Monte Carlo (MCMC) method using Gibbs sampling with Metropolis-Hastings algorithm and Tierney and Kadane (T-K) approximation. Bayes procedures have loss functions such as the squared error loss (SEL) and the balanced squared error loss (BSEL) function. For comparing results of proposed methods, some simulation experiments are performed with the different censoring schemes.