Estimation in generalized uniform distribution with progressively type-II censored sample

In this paper, the two-parameter generalized uniform distribution is considered as a lifetime model and its distributional and reliability characteristics are discussed. The maximum likelihood and Bayes estimates for both parameters are derived when the lifetime data are progressively type-II censored. The asymptotic confidence intervals and highest posterior density (HPD) credible intervals for the parameters are also discussed. For carrying out the Bayes estimation, the Metropolis-Hastings algorithm is used for generating a Markov chain Monte Carlo (MCMC) sample from the posterior distribution. The performance of the derived estimates is illustrated using a Monte Carlo simulation study. Finally, two real data sets are analyzed with respect to the estimation methods discussed.