Estimation of Generalized Pareto under an Adaptive Type-II Progressive Censoring
- 1 Mathematics Department, Faculty of Science, A1-Azhar University, Nasr-City, Cairo, Egypt
- 2 Faculty of Science, Islamic University, Madinah, Saudi Arabia
- 3 Mathematics Department, Sohag University, Sohag, Egypt
- 4 Mathematics Department, Faculty of Science, A1-Azhar University, Nasr-City, Cairo, Egypt
Abstract
In this paper, based on a new type of censoring scheme called an adaptive type-II progressive censoring scheme intro duce by Ng et al. [1], Naval Research Logistics is considered. Based on this type of censoring the maximum likelihood estimation (MLE), Bayes estimation, and parametric bootstrap method are used for estimating the unknown parameters. Also, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Point estimation and confidence intervals based on maximum likelihood and bootstrap method are also proposed. The approximate Bayes estimators obtained under the assumptions of non-infor mative priors, are compared with the maximum likelihood estimators. Numerical examples using real data set are pre sented to illustrate the methods of inference developed here. Finally, the maximum likelihood, bootstrap and the differ ent Bayes estimates are compared via a Monte Carlo simulation study.
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