Parameter Estimation and Prediction for the Generalized Half- Normal Distribution under Progressive Hybrid Censoring

In this paper, the problem of estimating unknown parameters of a general- ized half- normal distribution is considered under Type II progressive hybrid censoring which is a combination of Type II progressive and hybrid censoring schemes. We obtain maximum likelihood estimators of parameters and also construct asymptotic intervals using the observed Fisher information matrix. Further Bayes estimates are computed under the squared error loss function by applying dierent approximation methods. We also obtain prediction estimates and prediction intervals of censored observations. The performance of dierent methods is compared using Monte Carlo simulations and a real data set is analyzed for illustrative purposes.