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Bayesian estimation for Poisson process models with grouped data and covariate

This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process model for grouped failure data from a repairable system with covariate. The extended powerlaw model with a recurrence rate that incorporates both time and covariate effect is compared to the powerlaw, lo...

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Main Authors: Arasan, Jayanthi, Loh, Yue Fang
Format: Article
Language:English
Published: Institute of Engineering Mathematics, UniMAP 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30360/1/30360.pdf
http://psasir.upm.edu.my/id/eprint/30360/
https://amci.unimap.edu.my/index.php/vol-2-issue-2-2013
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spelling my.upm.eprints.303602019-11-25T07:46:35Z http://psasir.upm.edu.my/id/eprint/30360/ Bayesian estimation for Poisson process models with grouped data and covariate Arasan, Jayanthi Loh, Yue Fang This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process model for grouped failure data from a repairable system with covariate. The extended powerlaw model with a recurrence rate that incorporates both time and covariate effect is compared to the powerlaw, log-linear and HPP models. We propose the use of both informative and noninformative priors depending on the nature of the parameter. The MCMC technique is utilized to obtain samples from the posterior distribution which was implemented via WinBUGS. We then apply the Bayesian Deviance Information Criteria (DIC) to select the best model for real data from ball bearing failures where information regarding previous failures are available. The credible interval is used to check the significance of the parameters of the selected model. We also used the posterior predictive distribution for model checking by comparing the observed and posterior predictive mean number of failures. Institute of Engineering Mathematics, UniMAP 2013 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/30360/1/30360.pdf Arasan, Jayanthi and Loh, Yue Fang (2013) Bayesian estimation for Poisson process models with grouped data and covariate. Applied Mathematics and Computational Intelligence, 2 (2). pp. 217-227. ISSN 2289-1315; ESSN: 2289-1323 https://amci.unimap.edu.my/index.php/vol-2-issue-2-2013
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process model for grouped failure data from a repairable system with covariate. The extended powerlaw model with a recurrence rate that incorporates both time and covariate effect is compared to the powerlaw, log-linear and HPP models. We propose the use of both informative and noninformative priors depending on the nature of the parameter. The MCMC technique is utilized to obtain samples from the posterior distribution which was implemented via WinBUGS. We then apply the Bayesian Deviance Information Criteria (DIC) to select the best model for real data from ball bearing failures where information regarding previous failures are available. The credible interval is used to check the significance of the parameters of the selected model. We also used the posterior predictive distribution for model checking by comparing the observed and posterior predictive mean number of failures.
format Article
author Arasan, Jayanthi
Loh, Yue Fang
spellingShingle Arasan, Jayanthi
Loh, Yue Fang
Bayesian estimation for Poisson process models with grouped data and covariate
author_facet Arasan, Jayanthi
Loh, Yue Fang
author_sort Arasan, Jayanthi
title Bayesian estimation for Poisson process models with grouped data and covariate
title_short Bayesian estimation for Poisson process models with grouped data and covariate
title_full Bayesian estimation for Poisson process models with grouped data and covariate
title_fullStr Bayesian estimation for Poisson process models with grouped data and covariate
title_full_unstemmed Bayesian estimation for Poisson process models with grouped data and covariate
title_sort bayesian estimation for poisson process models with grouped data and covariate
publisher Institute of Engineering Mathematics, UniMAP
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/30360/1/30360.pdf
http://psasir.upm.edu.my/id/eprint/30360/
https://amci.unimap.edu.my/index.php/vol-2-issue-2-2013
_version_ 1651869041246076928
score 13.211869

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