File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

TitleBayesian Hierarchical Model for Change Point Detection in Multivariate Sequences
Authors
KeywordsChange points
Multivariate data
Nonlocal prior
Nonmaximum suppression
Poisson-Dirichlet process
Issue Date2021
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/tech/index.cfm?fuseaction=main
Citation
Technometrics, 2021, v. 64 n. 2, p. 1-23 How to Cite?
AbstractMotivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.
Persistent Identifierhttp://hdl.handle.net/10722/300549
ISSN
2023 Impact Factor: 2.3
2023 SCImago Journal Rankings: 1.114
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJin, H-
dc.contributor.authorYin, G-
dc.contributor.authorYUAN, B-
dc.contributor.authorJIANG, F-
dc.date.accessioned2021-06-18T14:53:34Z-
dc.date.available2021-06-18T14:53:34Z-
dc.date.issued2021-
dc.identifier.citationTechnometrics, 2021, v. 64 n. 2, p. 1-23-
dc.identifier.issn0040-1706-
dc.identifier.urihttp://hdl.handle.net/10722/300549-
dc.description.abstractMotivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/tech/index.cfm?fuseaction=main-
dc.relation.ispartofTechnometrics-
dc.subjectChange points-
dc.subjectMultivariate data-
dc.subjectNonlocal prior-
dc.subjectNonmaximum suppression-
dc.subjectPoisson-Dirichlet process-
dc.titleBayesian Hierarchical Model for Change Point Detection in Multivariate Sequences-
dc.typeArticle-
dc.identifier.emailYin, G: gyin@hku.hk-
dc.identifier.authorityYin, G=rp00831-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00401706.2021.1927848-
dc.identifier.scopuseid_2-s2.0-85108829200-
dc.identifier.hkuros322868-
dc.identifier.volume64-
dc.identifier.issue2-
dc.identifier.spage1-
dc.identifier.epage23-
dc.identifier.isiWOS:000666904500001-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats