Estimation of Parameters of the GIE Distribution Under Progressive Type-I Censoring

Abstract

In this paper, we consider generalized inverted exponential distribution which is capable of modeling various shapes of failure rates and aging processes. Based on progressive Type-I censored data, we consider the problem of estimation of parameters under classical and Bayesian approaches. In this regard, we obtain maximum likelihood estimates and Bayes estimates under squared error loss function. We also compute a 95% asymptotic confidence interval, bootstrap confidence intervals and highest posterior density (HPD) credible interval estimates. Finally, we analyze a real data set and conduct a Monte Carlo simulation study to compare the performance of the various proposed estimators.

Keywords

• Generalized inverted exponential distribution
• Progressive Type-I censoring scheme
• Maximum likelihood estimation
• Bayesian estimation
• Markov chain Monte Carlo
• Metropolis–Hasting algorithm