TITLE:
Estimation of Generalized Pareto under an Adaptive Type-II Progressive Censoring
AUTHORS:
Mohamed A. W. Mahmoud, Ahmed A. Soliman, Ahmed H. Abd Ellah, Rashad M. El-Sagheer
KEYWORDS:
Generalized Pareto (GP) Distribution; An Adaptive Type-II Progressive Censoring Scheme; Bayesian and Non-Bayesian Estimations; Gibbs and Metropolis Sampler; Bootstrap
JOURNAL NAME:
Intelligent Information Management,
Vol.5 No.3,
May
24,
2013
ABSTRACT: 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.