MCMC Technique to Study the Bayesian Estimation using Record Values from the Lomax Distribution

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...

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Bibliographic Details
Published in:International journal of computer applications 2013-01, Vol.73 (5), p.8-14
Main Authors: W Mahmoud, Mohamed A, Soliman, Ahmed A, Abd Ellah, Ahmed H, El-sagheer, Rashad M
Format: Article
Language:English
Subjects:
Approximation
Bayesian analysis
Computer simulation
Confidence intervals
Estimators
Mathematical models
Maximum likelihood estimators
Monte Carlo methods
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Summary: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.
ISSN:0975-8887
0975-8887
DOI:10.5120/12735-9617