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


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.

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M. Mahmoud, A. Soliman, A. Ellah and R. El-Sagheer, "Estimation of Generalized Pareto under an Adaptive Type-II Progressive Censoring," Intelligent Information Management, Vol. 5 No. 3, 2013, pp. 73-83. doi: 10.4236/iim.2013.53008.

Conflicts of Interest

The authors declare no conflicts of interest.


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