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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Institute of Engineering Mathematics, UniMAP
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30360/ http://psasir.upm.edu.my/id/eprint/30360/1/30360.pdf |
| _version_ | 1848846654776541184 |
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| author | Arasan, Jayanthi Loh, Yue Fang |
| author_facet | Arasan, Jayanthi Loh, Yue Fang |
| author_sort | Arasan, Jayanthi |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T09:06:09Z |
| format | Article |
| id | upm-30360 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:06:09Z |
| publishDate | 2013 |
| publisher | Institute of Engineering Mathematics, UniMAP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| spellingShingle | Arasan, Jayanthi Loh, Yue Fang Bayesian estimation for Poisson process models with grouped data and covariate |
| title | 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_short | Bayesian estimation for Poisson process models with grouped data and covariate |
| title_sort | bayesian estimation for poisson process models with grouped data and covariate |
| url | http://psasir.upm.edu.my/id/eprint/30360/ http://psasir.upm.edu.my/id/eprint/30360/ http://psasir.upm.edu.my/id/eprint/30360/1/30360.pdf |