ESTIMATION IN EXPONENTIAL POWER DISTRIBUTION UNDER PROGRESSIVE TYPE-II CENSORING WITH BINOMIAL REMOVALS

In the present paper, we reflect consideration on the estimation of the parameters of an exponential power distribution under progressive type-II censoring with random removals where the number of units removed at each failure time follows a binomial distribution. The classical and Bayesian approaches are used to derive both point and interval estimates of the parameters. Monte Carlo simulation and Gibbs sampling of the Markov chain Monte Carlo (MCMC) techniques are used to generate progressive type-II censored samples then these samples are used to estimate the unknown parameters and compare the overall performance of both the approaches. Finally, a real-life data set is considered for illustrative purposes.