Parameter Estimation in Marshall-Olkin Exponential Distribution Under Type-I Hybrid Censoring Scheme

The two most popular censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is the mixture of Type-I and Type-II censoring scheme. In this article, we consider the estimation of parameters of Marshall-Olkin exponential distribution based on Type-I Hybrid censored data. Both classical and Bayesian methodology have been discussed to estimate the model parameters. In classical set-up maximum likelihood estimators (MLEs) of the parameters have been obtained by using Newton-Raphson method and also by using Fisher information matrix 95% asymptotic confidence intervals are provided. In Bayesian set-up Lindley’s approximation technique and Markov Chain Monte Carlo (MCMC) technique have been used to compute the Bayes estimators. Further, we have also provided highest posterior density (HPD) intervals of the parameters based on MCMC samples. To compare the performances of the estimators Monte Carlo simulation has performed and one data set is analysed for illustrative purpose of the study.