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Article: Factor Models for High-Dimensional Tensor Time Series

TitleFactor Models for High-Dimensional Tensor Time Series
Authors
KeywordsAutocovariance matrices
Cross-covariance matrices
Dimension reduction
Dynamic transport network
Eigen-analysis
Issue Date2021
PublisherTaylor and Francis, published in association with American Statistical Association. The Journal's web site is located at http://www.tandfonline.com/toc/uasa20/current
Citation
Journal of the American Statistical Association, 2021, Epub 2021-05-19 How to Cite?
AbstractLarge tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this paper we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. Two estimation procedures are presented along with their theoretical properties and simulation results. Two applications are used to illustrate the model and its interpretations.
Persistent Identifierhttp://hdl.handle.net/10722/299054
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 3.922
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, R-
dc.contributor.authorYang, D-
dc.contributor.authorZhang, C-
dc.date.accessioned2021-04-28T02:25:35Z-
dc.date.available2021-04-28T02:25:35Z-
dc.date.issued2021-
dc.identifier.citationJournal of the American Statistical Association, 2021, Epub 2021-05-19-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10722/299054-
dc.description.abstractLarge tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this paper we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. Two estimation procedures are presented along with their theoretical properties and simulation results. Two applications are used to illustrate the model and its interpretations.-
dc.languageeng-
dc.publisherTaylor and Francis, published in association with American Statistical Association. The Journal's web site is located at http://www.tandfonline.com/toc/uasa20/current-
dc.relation.ispartofJournal of the American Statistical Association-
dc.rightsAccepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectAutocovariance matrices-
dc.subjectCross-covariance matrices-
dc.subjectDimension reduction-
dc.subjectDynamic transport network-
dc.subjectEigen-analysis-
dc.titleFactor Models for High-Dimensional Tensor Time Series-
dc.typeArticle-
dc.identifier.emailYang, D: dyanghku@hku.hk-
dc.identifier.authorityYang, D=rp02487-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01621459.2021.1912757-
dc.identifier.scopuseid_2-s2.0-85106344670-
dc.identifier.hkuros322226-
dc.identifier.volumeEpub 2021-05-19-
dc.identifier.isiWOS:000652148700001-
dc.publisher.placeUnited States-

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