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dc.contributor.authorChagny, Gaëlle
dc.contributor.authorChannarond, Antoine
dc.contributor.authorHoang, Van Ha
dc.contributor.authorRoche, Angelina
dc.date.accessioned2020-10-26T08:26:44Z
dc.date.available2020-10-26T08:26:44Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21159
dc.language.isoenen
dc.subjecttwo-class mixture modelen
dc.subjectGoldenshluger and Lepski methoden
dc.subject.ddc519en
dc.titleAdaptive nonparametric estimation of a component density in a two-class mixture modelen
dc.typeDocument de travail / Working paper
dc.description.abstractenA two-class mixture model, where the density of one of the components is known, is considered. We address the issue of the nonparametric adaptive estimation of the unknown probability density of the second component. We propose a randomly weighted kernel estimator with a fully data-driven bandwidth selection method, in the spirit of the Goldenshluger and Lepski method. An oracle-type inequality for the pointwise quadratic risk is derived as well as convergence rates over Hölder smoothness classes. The theoretical results are illustrated by numerical simulations.en
dc.identifier.citationpages21en
dc.relation.ispartofseriestitleCahier de recherche CEREMADEen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02909601en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2020-07
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2020-10-26T08:20:25Z
hal.person.labIds91
hal.person.labIds91
hal.person.labIds91
hal.person.labIds60


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