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Article: A variable selection approach to multiple change-points detection with ordinal data

TitleA variable selection approach to multiple change-points detection with ordinal data
Authors
Keywordslatent variable
multiple change-points
ordinal data
probit model
reversible jump Markov chain Monte Carlo
Issue Date2020
PublisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII
Citation
Statistics and its Interface, 2020, v. 13 n. 2, p. 251-260 How to Cite?
AbstractChange-point detection has been studied extensively with continuous data, while much less research has been carried out for categorical data. Focusing on ordinal data, we reframe the change-point detection problem in a Bayesian variable selection context. We propose a latent probit model in conjunction with reversible jump Markov chain Monte Carlo to estimate both the number and locations of change-points with ordinal data. We conduct extensive simulation studies to assess the performance of our method. As an illustration, we apply the new method to detect changes in the ordinal data from the north Atlantic tropical cyclone record, which has an indication of global warming in the past decades.
Persistent Identifierhttp://hdl.handle.net/10722/288180
ISSN
2019 Impact Factor: 0.412
2015 SCImago Journal Rankings: 0.481

 

DC FieldValueLanguage
dc.contributor.authorLam, CK-
dc.contributor.authorJIN, H-
dc.contributor.authorJiang, F-
dc.contributor.authorYin, G-
dc.date.accessioned2020-10-05T12:09:02Z-
dc.date.available2020-10-05T12:09:02Z-
dc.date.issued2020-
dc.identifier.citationStatistics and its Interface, 2020, v. 13 n. 2, p. 251-260-
dc.identifier.issn1938-7989-
dc.identifier.urihttp://hdl.handle.net/10722/288180-
dc.description.abstractChange-point detection has been studied extensively with continuous data, while much less research has been carried out for categorical data. Focusing on ordinal data, we reframe the change-point detection problem in a Bayesian variable selection context. We propose a latent probit model in conjunction with reversible jump Markov chain Monte Carlo to estimate both the number and locations of change-points with ordinal data. We conduct extensive simulation studies to assess the performance of our method. As an illustration, we apply the new method to detect changes in the ordinal data from the north Atlantic tropical cyclone record, which has an indication of global warming in the past decades.-
dc.languageeng-
dc.publisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII-
dc.relation.ispartofStatistics and its Interface-
dc.rightsStatistics and its Interface. Copyright © International Press.-
dc.subjectlatent variable-
dc.subjectmultiple change-points-
dc.subjectordinal data-
dc.subjectprobit model-
dc.subjectreversible jump Markov chain Monte Carlo-
dc.titleA variable selection approach to multiple change-points detection with ordinal data-
dc.typeArticle-
dc.identifier.emailYin, G: gyin@hku.hk-
dc.identifier.authorityYin, G=rp00831-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.4310/SII.2020.v13.n2.a9-
dc.identifier.scopuseid_2-s2.0-85079554929-
dc.identifier.hkuros315656-
dc.identifier.volume13-
dc.identifier.issue2-
dc.identifier.spage251-
dc.identifier.epage260-
dc.publisher.placeUnited States-
dc.identifier.issnl1938-7989-

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