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- Publisher Website: 10.1080/01621459.2021.1912757
- Scopus: eid_2-s2.0-85106344670
- WOS: WOS:000652148700001
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Article: Factor Models for High-Dimensional Tensor Time Series
| Title | Factor Models for High-Dimensional Tensor Time Series |
|---|---|
| Authors | |
| Keywords | Autocovariance matrices Cross-covariance matrices Dimension reduction Dynamic transport network Eigen-analysis |
| Issue Date | 2021 |
| Publisher | Taylor 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? |
| Abstract | Large 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 Identifier | http://hdl.handle.net/10722/299054 |
| ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 3.922 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chen, R | - |
| dc.contributor.author | Yang, D | - |
| dc.contributor.author | Zhang, C | - |
| dc.date.accessioned | 2021-04-28T02:25:35Z | - |
| dc.date.available | 2021-04-28T02:25:35Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | Journal of the American Statistical Association, 2021, Epub 2021-05-19 | - |
| dc.identifier.issn | 0162-1459 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/299054 | - |
| dc.description.abstract | Large 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.language | eng | - |
| dc.publisher | Taylor 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.ispartof | Journal of the American Statistical Association | - |
| dc.rights | Accepted 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.subject | Autocovariance matrices | - |
| dc.subject | Cross-covariance matrices | - |
| dc.subject | Dimension reduction | - |
| dc.subject | Dynamic transport network | - |
| dc.subject | Eigen-analysis | - |
| dc.title | Factor Models for High-Dimensional Tensor Time Series | - |
| dc.type | Article | - |
| dc.identifier.email | Yang, D: dyanghku@hku.hk | - |
| dc.identifier.authority | Yang, D=rp02487 | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1080/01621459.2021.1912757 | - |
| dc.identifier.scopus | eid_2-s2.0-85106344670 | - |
| dc.identifier.hkuros | 322226 | - |
| dc.identifier.volume | Epub 2021-05-19 | - |
| dc.identifier.isi | WOS:000652148700001 | - |
| dc.publisher.place | United States | - |