Multiple change-point detection of multivariate mean vectors with the Bayesian approach
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cheon, Sooyoung | - |
dc.contributor.author | Kim, Jaehee | - |
dc.date.accessioned | 2021-09-08T05:10:33Z | - |
dc.date.available | 2021-09-08T05:10:33Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2010-02-01 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/117006 | - |
dc.description.abstract | Bayesian multiple change-point models are proposed for multivariate means. The models require that the data be from a multivariate normal distribution with a truncated Poisson prior for the number of change-points and conjugate priors for the distributional parameters. We apply the stochastic approximation Monte Carlo (SAMC) algorithm to the multiple change-point detection problems. Numerical results show that SAMC makes a significant improvement over RJMCMC for complex Bayesian model selection problems in change-point estimation. (C) 2009 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | MONTE-CARLO COMPUTATION | - |
dc.subject | STOCHASTIC-APPROXIMATION | - |
dc.subject | BEEF PALATABILITY | - |
dc.subject | RANDOM-VARIABLES | - |
dc.subject | SEQUENCE | - |
dc.subject | INFERENCE | - |
dc.subject | MODELS | - |
dc.title | Multiple change-point detection of multivariate mean vectors with the Bayesian approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cheon, Sooyoung | - |
dc.identifier.doi | 10.1016/j.csda.2009.09.003 | - |
dc.identifier.scopusid | 2-s2.0-70350567753 | - |
dc.identifier.wosid | 000272110900013 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.54, no.2, pp.406 - 415 | - |
dc.relation.isPartOf | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.title | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.volume | 54 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 406 | - |
dc.citation.endPage | 415 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | MONTE-CARLO COMPUTATION | - |
dc.subject.keywordPlus | STOCHASTIC-APPROXIMATION | - |
dc.subject.keywordPlus | BEEF PALATABILITY | - |
dc.subject.keywordPlus | RANDOM-VARIABLES | - |
dc.subject.keywordPlus | SEQUENCE | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | MODELS | - |
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