Interfailure Data with Constant Hazard Function in the Presence of Change-Points
Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Instituto Nacional de Estatística | Statistics Portugal
2007-06-01
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| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/49 |
| _version_ | 1828148567461593088 |
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| author | Jorge Alberto Achcar Selene Loibel Marinho G. Andrade |
| author_facet | Jorge Alberto Achcar Selene Loibel Marinho G. Andrade |
| author_sort | Jorge Alberto Achcar |
| collection | DOAJ |
| description |
Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].
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| first_indexed | 2024-04-11T21:17:48Z |
| format | Article |
| id | doaj.art-a5afef06421247d7936214c255959dd3 |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-04-11T21:17:48Z |
| publishDate | 2007-06-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-a5afef06421247d7936214c255959dd32022-12-22T04:02:46ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712007-06-015210.57805/revstat.v5i2.49Interfailure Data with Constant Hazard Function in the Presence of Change-PointsJorge Alberto Achcar 0Selene Loibel 1Marinho G. Andrade 2Universidade Federal de São CarlosUniversidade Federal de São CarlosUniversidade Federal de São Carlos Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8]. https://revstat.ine.pt/index.php/REVSTAT/article/view/49constant hazardchange-pointsGibbs samplingMCMC algorithms |
| spellingShingle | Jorge Alberto Achcar Selene Loibel Marinho G. Andrade Interfailure Data with Constant Hazard Function in the Presence of Change-Points Revstat Statistical Journal constant hazard change-points Gibbs sampling MCMC algorithms |
| title | Interfailure Data with Constant Hazard Function in the Presence of Change-Points |
| title_full | Interfailure Data with Constant Hazard Function in the Presence of Change-Points |
| title_fullStr | Interfailure Data with Constant Hazard Function in the Presence of Change-Points |
| title_full_unstemmed | Interfailure Data with Constant Hazard Function in the Presence of Change-Points |
| title_short | Interfailure Data with Constant Hazard Function in the Presence of Change-Points |
| title_sort | interfailure data with constant hazard function in the presence of change points |
| topic | constant hazard change-points Gibbs sampling MCMC algorithms |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/49 |
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