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Reseach Article

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

by Mohamed A. W. Mahmoud, Ahmed A. Soliman, Ahmed H. Abd Ellah, Rashad M. El-sagheer
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

@article{ 10.5120/12735-9617,
author = { Mohamed A. W. Mahmoud, Ahmed A. Soliman, Ahmed H. Abd Ellah, Rashad M. El-sagheer },
title = { MCMC Technique to Study the Bayesian Estimation using Record Values from the Lomax Distribution },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 5 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number5/12735-9617/ },
doi = { 10.5120/12735-9617 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:15.041530+05:30
%A Mohamed A. W. Mahmoud
%A Ahmed A. Soliman
%A Ahmed H. Abd Ellah
%A Rashad M. El-sagheer
%T MCMC Technique to Study the Bayesian Estimation using Record Values from the Lomax Distribution
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 5
%P 8-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Lomax distribution Bayesian and non-Bayesian estimations Gibbs and Metropolis sampler Bootstrap methods. ifx