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dc.contributor.authorNguyen, Minh-Lien*
dc.contributor.authorLacour, Claire*
dc.contributor.authorRivoirard, Vincent*
dc.date.accessioned2019-07-24T11:23:58Z
dc.date.available2019-07-24T11:23:58Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19400
dc.language.isoenen
dc.subjectsparsity
dc.subjectgreedy algorithm
dc.subjectkernel density estima- tors
dc.subjectminimax rates
dc.subjecthigh dimension
dc.subjectconditional density
dc.subjectnonparametric inference
dc.subject.ddc515en
dc.titleAdaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation
dc.typeRapport
dc.description.abstractenThis paper studies the estimation of the conditional density f (x, ·) of Y i given X i = x, from the observation of an i.i.d. sample (X i , Y i) ∈ R d , i = 1,. .. , n. We assume that f depends only on r unknown components with typically r d. We provide an adaptive fully-nonparametric strategy based on kernel rules to estimate f. To select the bandwidth of our kernel rule, we propose a new fast iterative algorithm inspired by the Rodeo algorithm (Wasserman and Lafferty (2006)) to detect the sparsity structure of f. More precisely, in the minimax setting, our pointwise estimator, which is adaptive to both the regularity and the sparsity, achieves the quasi-optimal rate of convergence. Its computational complexity is only O(dn log n).
dc.publisher.cityParisen
dc.identifier.citationpages45
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02085677
dc.subject.ddclabelAnalyseen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-07-01T12:34:28Z
hal.person.labIds40*
hal.person.labIds29*
hal.person.labIds60*


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