An ℓ 1 -oracle inequality for the Lasso in finite mixture of multivariate Gaussian regression models.
In: ISSN: 1292-8100 ; EISSN: 1262-3318, 2015
Online
academicJournal
Zugriff:
International audience ; We consider a multivariate finite mixture of Gaussian regression models for high-dimensional data, where the number of covariates and the size of the response may be much larger than the sample size. We provide an ℓ 1 -oracle inequality satisfied by the Lasso estimator according to the Kullback-Leibler loss. This result is an extension of the ℓ 1 -oracle inequality established by Meynet in the multivariate case. We focus on the Lasso for its ℓ 1 -regularization properties rather than for the variable selection procedure, as it was done in Städler et al.
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An ℓ 1 -oracle inequality for the Lasso in finite mixture of multivariate Gaussian regression models.
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| Autor/in / Beteiligte Person: | Devijver, Emilie ; Model selection in statistical learning (SELECT) ; Inria Saclay - Ile de France ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO) ; Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS) ; Laboratoire de Mathématiques d'Orsay (LM-Orsay) ; Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS) |
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| Zeitschrift: | ISSN: 1292-8100 ; EISSN: 1262-3318, 2015 |
| Veröffentlichung: | HAL CCSD ; EDP Sciences, 2015 |
| Medientyp: | academicJournal |
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