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Minimax optimal estimators for general additive functional estimation

hal.structure.identifierModélisation aléatoire de Paris X [MODAL'X]
dc.contributor.authorCollier, Olivier
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorComminges, Laëtitia
HAL ID: 11573
dc.date.accessioned2019-10-12T10:32:18Z
dc.date.available2019-10-12T10:32:18Z
dc.date.issued2019-08
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20105
dc.language.isoenen
dc.subjectMinimax estimationen
dc.subjectadditive functionalen
dc.subjectsparsityen
dc.subjectpolynomial approximationen
dc.subject.ddc519en
dc.titleMinimax optimal estimators for general additive functional estimationen
dc.typeDocument de travail / Working paper
dc.description.abstractenIn this paper, we observe a sparse mean vector through Gaussian noise and we aim at estimating some additive functional of the mean in the minimax sense. More precisely, we generalize the results of (Collier et al., 2017, 2019) to a very large class of functionals. The optimal minimax rate is shown to depend on the polynomial approximation rate of the marginal functional, and optimal estimators achieving this rate are built.en
dc.publisher.nameCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages19en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02273511en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2019
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-10-12T10:30:27Z
hal.author.functionaut
hal.author.functionaut


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