Estimation of Generalized Inverted Exponential Distribution based on Adaptive Type-II Progressive Censoring Data

Abstract: In this paper, we consider generalized inverted exponential (GIE) model. Three issues represent the purpose of this paper. First, based on adaptive progressively Type-II censored data, we derive the maximum likelihood estimators (MLE) of the model parameters as well as the reliability and hazard rate functions. Next, the Bayes estimates are evaluated by applying Markov chain Monte Carlo method under the balanced squared error (BSEL) loss function and balanced linear exponential (BLINEX) loss function. Based on the asymptotic distributions of the MLEs and MCMC samples, we compute asymptotic confidence interval and symmetric credible interval along with the coverage probability. We analyze a real data set to illustrate the results derived. Simulation studies are conducted to compare the performances of the Bayes estimators with the maximum likelihood estimators. Finally, a numerical example is presented to illustrate the methods developed