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MCMC Technique to Study the Bayesian Estimation using Record Values from the Lomax Distribution
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 73 - Number 5 |
| Year of Publication: 2013 |
| Authors: Mohamed A. W. Mahmoud, Ahmed A. Soliman, Ahmed H. Abd Ellah, Rashad M. El-sagheer |
10.5120/12735-9617
|
Mohamed A. W. Mahmoud, Ahmed A. Soliman, Ahmed H. Abd Ellah, Rashad M. El-sagheer . MCMC Technique to Study the Bayesian Estimation using Record Values from the Lomax Distribution. International Journal of Computer Applications. 73, 5 ( July 2013), 8-14. DOI=10.5120/12735-9617
Abstract
In this paper, the Bayes estimators of the unknown parameters of the Lomax distribution under the assumptions of gamma priors on both the shape and scale parameters are considered. The Bayes estimators cannot be obtained in explicit forms. So we propose Markov Chain Monte Carlo (MCMC) techniques to generate samples from the posterior distributions and in turn computing the Bayes estimators. Point estimation and confidence intervals based on maximum likelihood and bootstrap methods are also proposed. The approximate Bayes estimators obtained under the assumptions of non-informative priors, are compared with the maximum likelihood estimators using Monte Carlo simulations. One real data set has been analyzed for illustrative purposes.
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