Estimating the parameters of a truncated normal distribution under progressive type II censoring

The problem of making statistical inference for a truncated normal distribution is considered under progressive type II censoring. Maximum likelihood and Bayesian approaches are used to obtain point and interval estimates of unknown parameters. Bayes estimates are derived with respect to informative and non-informative prior distributions when the loss function is squared error. Monte Carlo simulations and real data analysis are presented to study the performance of proposed methods. Finally, optimal censoring plans based on the expected Fisher information matrix are discussed under different optimality criteria.