On hybrid censored inverted exponential distribution

The present study deals with the estimation procedure of the parameter of inverted exponential distribution based hybrid censored data. For estimation purpose, we consider both, Classical and Bayesian method of estimation. In classical set up, the maximum likelihood estimate of the parameter with its standard error and confidence interval are computed. Further, by assuming Jeffrey’s invariant and gamma priors of the unknown parameter, Bayes estimate along with its posterior standard error and highest posterior density credible interval of the parameter are obtained. Markov Chain Monte Carlo technique such as Metropolis-Hastings algorithm has been utilized to generate simulated draws from the posterior density of the parameter. A real data set representing the survival times (in days) of guinea pigs injected with different doses of tubercle bacilli has been analyzed for illustrative purpose.