Mostrar el registro sencillo del ítem

An introduction to dimension reduction in nonparametric kernel regression

hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
dc.contributor.authorGIRARD, Stéphane
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jerôme
dc.contributor.editorD. Fraix-Burnet
dc.contributor.editorD. Valls-Gabaud
dc.date.accessioned2024-04-04T02:18:25Z
dc.date.available2024-04-04T02:18:25Z
dc.date.created2014
dc.date.issued2014
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189312
dc.description.abstractEnNonparametric regression is a powerful tool to estimate nonlinear relations between some predictors and a response variable. However, when the number of predictors is high, nonparametric estimators may suffer from the curse of dimensionality. In this chapter, we show how a dimension reduction method (namely Sliced Inverse Regression) can be combined with nonparametric kernel regression to overcome this drawback. The methods are illustrated both on simulated datasets as well as on an astronomy dataset using the R software.
dc.language.isoen
dc.publisherEDP Sciences
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.source.titleRegression methods for astrophysics
dc.title.enAn introduction to dimension reduction in nonparametric kernel regression
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1051/eas/1466012
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
bordeaux.page167-196
bordeaux.volume66
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingRegression methods for astrophysics
hal.identifierhal-00977512
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00977512v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Regression%20methods%20for%20astrophysics&rft.date=2014&rft.volume=66&rft.spage=167-196&rft.epage=167-196&rft.au=GIRARD,%20St%C3%A9phane&SARACCO,%20Jer%C3%B4me&rft.genre=unknown


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem