Estimation of Generalized Pareto under an Adaptive Type-II Progressive Censoring

作     者：Mohamed A. W. Mahmoud Ahmed A. Soliman Ahmed H. Abd Ellah Rashad M. El-Sagheer 

作者机构：Faculty of Science Islamic University Madinah Saudi Arabia Mathematics Department Faculty of Science A1-Azhar University Nasr-City Cairo Egypt Mathematics Department Sohag University Sohag Egypt 

出 版 物：《Intelligent Information Management》 (智能信息管理（英文）)

年 卷 期：2013年第5卷第3期

页      面：73-83页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Generalized Pareto (GP) Distribution An Adaptive Type-II Progressive Censoring Scheme Bayesian and Non-Bayesian Estimations Gibbs and Metropolis Sampler Bootstrap 

摘      要：In this paper, based on a new type of censoring scheme called an adaptive type-II progressive censoring scheme introduce 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-informative priors, are compared with the maximum likelihood estimators. Numerical examples using real data set are presented to illustrate the methods of inference developed here. Finally, the maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo simulation study.