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dc.contributor.authorCollier, Olivier*
dc.contributor.authorComminges, Laëtitia*
dc.contributor.authorTsybakov, Alexandre*
dc.contributor.authorVerzelen, Nicolas*
dc.date.accessioned2020-06-12T09:54:58Z
dc.date.available2020-06-12T09:54:58Z
dc.date.issued2018
dc.identifier.issn0090-5364
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20881
dc.language.isoenen
dc.subjectNonasymptotic minimax estimationen
dc.subjectadaptive estimationen
dc.subjectlinear functionalen
dc.subjectsparsityen
dc.subjectunknown noise varianceen
dc.subject.ddc515en
dc.titleOptimal adaptive estimation of linear functionals under sparsityen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe consider the problem of estimation of a linear functional in the Gaussian sequence model where the unknown vector theta is an element of R-d belongs to a class of s-sparse vectors with unknown s. We suggest an adaptive estimator achieving a nonasymptotic rate of convergence that differs from the minimax rate at most by a logarithmic factor. We also show that this optimal adaptive rate cannot be improved when s is unknown. Furthermore, we address the issue of simultaneous adaptation to s and to the variance sigma(2) of the noise. We suggest an estimator that achieves the optimal adaptive rate when both s and sigma(2) are unknown.en
dc.relation.isversionofjnlnameAnnals of Statistics
dc.relation.isversionofjnlvol46en
dc.relation.isversionofjnlissue6en
dc.relation.isversionofjnldate2018
dc.relation.isversionofjnlpages3130 - 3150en
dc.relation.isversionofdoi10.1214/17-AOS1653en
dc.relation.isversionofjnlpublisherInstitute of Mathematical Statisticsen
dc.subject.ddclabelAnalyseen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2020-06-12T09:21:00Z
hal.person.labIds101*
hal.person.labIds60*
hal.person.labIds2579*
hal.person.labIds1002247*


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