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Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation

hal.structure.identifierLaboratoire de Mathématiques d'Orsay [LM-Orsay]
dc.contributor.authorVaret, Suzanne
hal.structure.identifierLaboratoire d'Analyse et de Mathématiques Appliquées [LAMA]
dc.contributor.authorLacour, Claire
HAL ID: 3484
hal.structure.identifierLaboratoire de Mathématiques d'Orsay [LM-Orsay]
dc.contributor.authorMassart, Pascal
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRivoirard, Vincent
dc.date.accessioned2019-03-25T10:56:47Z
dc.date.available2019-03-25T10:56:47Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18556
dc.language.isoenen
dc.subjectMultivariate density estimationen
dc.subjectBandwidth selectionen
dc.subjectKernel-based density estimationen
dc.subject.ddc519en
dc.titleNumerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimationen
dc.typeDocument de travail / Working paper
dc.description.abstractenKernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the bandwidth selection remains a challenging issue in terms of balancing algorithmic performance and statistical relevance. The purpose of this paper is to compare a recently developped bandwidth selection method for kernel density estimation to those which are commonly used by now (at least those which are implemented in the R-package). This new method is called Penalized Comparison to Overfitting (PCO). It has been proposed by some of the authors of this paper in a previous work devoted to its statistical relevance from a purely theoretical perspective. It is compared here to other usual bandwidth selection methods for univariate and also multivariate kernel density estimation on the basis of intensive simulation studies. In particular, cross-validation and plug-in criteria are numerically investigated and compared to PCO. The take home message is that PCO can outperform the classical methods without algorithmic additionnal cost.en
dc.publisher.nameCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages50en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02002275en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2019-02
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-03-25T10:54:12Z
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