Estimating the parameters of a generalized inverted exponential distribution based on adaptive type II hybrid progressive censoring with application

In this paper, we address the problems of estimation when lifetime data following generalized inverted exponential (GIE) distribution are observed under adaptive Type II hybrid progressive censoring. The maximum likelihood estimates of the unknown parameters are evaluated by using the stochastic expectation-maximization algorithm. From Bayesian aspect, the Bayes estimates and the highest posterior density (HPD) credible intervals of the unknown parameters are derived by applying the Monte Carlo Markov chain (MCMC) method. The approximate confidence intervals and Bootstrap confidence intervals are also developed. Monte Carlo simulations are implemented and observations are given. Finally, the micro plasma spread factor data is analyzed to illustrate the application and development of the theoretical techniques.