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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDOSSAL, Charles
hal.structure.identifierLaboratoire de Probabilités et Modèles Aléatoires [LPMA]
hal.structure.identifierModel selection in statistical learning [SELECT]
hal.structure.identifierLaboratoire de Mathématiques d'Orsay [LM-Orsay]
dc.contributor.authorLE PENNEC, Erwan
hal.structure.identifierCentre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
dc.contributor.authorMALLAT, Stéphane
dc.date.accessioned2024-04-04T02:30:55Z
dc.date.available2024-04-04T02:30:55Z
dc.date.created2009-12
dc.date.issued2011-01
dc.identifier.issn0165-1684
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190276
dc.description.abstractEnTo estimate geometrically regular images in the white noise model and obtain an adaptive near asymptotic minimaxity result, we consider a model selection based bandlet estimator. This bandlet estimator combines the best basis selection behaviour of the model selection and the approximation properties of the bandlet dictionary. We derive its near asymptotic minimaxity for geometrically regular images as an example of model selection with general dictionary of orthogonal bases. This paper is thus also a self contained tutorial on model selection with orthogonal bases dictionary.
dc.description.sponsorshipParcimonie en Statistique - ANR-09-BLAN-0128
dc.language.isoen
dc.publisherElsevier
dc.title.enBandlet Image Estimation with Model Selection
dc.typeArticle de revue
dc.identifier.doi10.1016/j.sigpro.2011.01.013
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
dc.subject.halInformatique [cs]/Traitement des images
dc.identifier.arxiv0809.3092
bordeaux.journalSignal Processing
bordeaux.page2743-2753
bordeaux.volume91
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue12
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00321965
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00321965v1
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