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Adaptive Lasso and group-Lasso for functional Poisson regression

dc.contributor.authorIvanoff, Stéphane
dc.contributor.authorPicard, F.
HAL ID: 177498
dc.contributor.authorRivoirard, Vincent
dc.date.accessioned2015-01-07T15:52:45Z
dc.date.available2015-01-07T15:52:45Z
dc.date.issued2016
dc.identifier.issn1532-4435
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14499
dc.language.isoenen
dc.subjectFunctional Poisson regression
dc.subjectadaptive lasso
dc.subjectadaptive group-lasso
dc.subjectcalibra-tion
dc.subjectconcentration
dc.subject.ddc519en
dc.titleAdaptive Lasso and group-Lasso for functional Poisson regression
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherLBBE, UMR CNRS 5558 Univ. Lyon 1,;France
dc.description.abstractenHigh dimensional Poisson regression has become a standard framework for the analysis ofmassive counts datasets. In this work we estimate the intensity function of the Poissonregression model by using a dictionary approach, which generalizes the classical basis ap-proach, combined with a Lasso or a group-Lasso procedure. Selection depends on penaltyweights that need to be calibrated. Standard methodologies developed in the Gaussianframework can not be directly applied to Poisson models due to heteroscedasticity. Here weprovide data-driven weights for the Lasso and the group-Lasso derived from concentrationinequalities adapted to the Poisson case. We show that the associated Lasso and group-Lassoprocedures are theoretically optimal in the oracle approach. Simulations are used to assessthe empirical performance of our procedure, and an original application to the analysis ofNext Generation Sequencing data is provided.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameJournal of Machine Learning Research
dc.relation.isversionofjnlvol17
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages1-46
dc.relation.isversionofjnlpublisherMIT Press
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2016-10-06T14:23:22Z


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