Classical and Bayesian inference based on progressive type-II hybrid censored data from the Poisson-Exponential distribution

In this paper, the problem of estimating unknown parameters is investigated when lifetime data following Poisson-exponential distribution under classical and Bayesian frameworks based on progressively type-II hybrid censored data. We compute point and associated interval estimates under classical and Bayesian approaches. For point estimates in the problem of estimation, we compute maximum likelihood estimators of model using Expectation-Maximization (EM) and Stochastic Expectation-Maximization (SEM) algorithms under classical approach, these algorithms are easily implemented. We compute Bayes estimates with the help of Lindley and importance sampling technique under informative and non-informative priors using different loss functions namely squared error, LINEX as well as general entropy in Bayesian framework. The associated interval estimates are obtained using the Fisher information matrix and Chen and Shao method respectively under classical and Bayesian approaches. We analysis real data set, and conduct Monte Carlo simulation study for the comparison of various proposed methods. Finally, we present a conclusion.