Change Detection in The Covariance Structure of High-Dimensional Gaussian Low-Rank Models
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | BEISSON, Remi | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | VALLET, Pascal | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | GIREMUS, Audrey
IDREF: 163238766 | |
| dc.contributor.author | GINOLHAC, G. | |
| dc.date.accessioned | 2023-12-20T09:14:45Z | |
| dc.date.available | 2023-12-20T09:14:45Z | |
| dc.date.issued | 2021-07-11 | |
| dc.date.conference | 2021-07-11 | |
| dc.identifier.uri | oai:crossref.org:10.1109/ssp49050.2021.9513795 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/186747 | |
| dc.description.abstractEn | This paper is devoted to the problem of testing equality between the covariance matrices of L multivariate Gaussian time series with dimension M, in the context where each of the L covariance matrices is the sum of a low-rank K component and the identity matrix. Assuming N 1 , …, N L samples are available for each time series, a new test statistic, based on the eigenvalues of the L sample covariance matrices (SCM) of each time series as well as the eigenvalues of a pooled SCM mixing the N 1 +…+N L available samples, is pro-posed and proved to be consistent in the high dimensional regime in which M, N 1 , …, N L converge to infinity at the same rate, while K and L are kept fixed. Numerical simulations show that the proposed test statistic is competitive with other relevant methods for moderate values of M, N 1 , …, N L . | |
| dc.language.iso | EN | en_US |
| dc.publisher | IEEE | en_US |
| dc.source | crossref | |
| dc.subject.en | Change detection | |
| dc.subject.en | Covariance | |
| dc.subject.en | Spiked models | |
| dc.subject.en | Random matrix theory | |
| dc.title.en | Change Detection in The Covariance Structure of High-Dimensional Gaussian Low-Rank Models | |
| dc.type | Communication dans un congrès | en_US |
| dc.identifier.doi | 10.1109/ssp49050.2021.9513795 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | en_US |
| bordeaux.hal.laboratories | IMS : Laboratoire de l'Intégration du Matériau au Système - UMR 5218 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.conference.title | 2021 IEEE Statistical Signal Processing Workshop (SSP) | en_US |
| bordeaux.country | br | en_US |
| bordeaux.conference.city | Rio de Janeiro | en_US |
| bordeaux.import.source | dissemin | |
| hal.invited | oui | en_US |
| hal.proceedings | oui | en_US |
| hal.conference.end | 2021-07-14 | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021-07-11&rft.au=BEISSON,%20Remi&VALLET,%20Pascal&GIREMUS,%20Audrey&GINOLHAC,%20G.&rft.genre=unknown |
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