Afficher la notice abrégée

A new sliced inverse regression method for multivariate response

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorCOUDRET, Raphaël
hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
dc.contributor.authorGIRARD, Stéphane
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jerome
dc.date.accessioned2024-04-04T02:21:16Z
dc.date.available2024-04-04T02:21:16Z
dc.date.created2013
dc.date.issued2014-09
dc.identifier.issn0167-9473
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189558
dc.description.abstractEnA semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate square root of n of the estimated EDR space is shown. The choice of the dimension of the EDR space is discussed. Moreover, a way to cluster components of y related to the same EDR space is provided. Thus, the proposed multivariate SIR method can be used properly on each cluster instead of blindly applying it on all components of y. The numerical performances of multivariate SIR are illustrated on a simulation study. Applications to a remote sensing dataset and to the Minneapolis elementary schools data are also provided. Although the proposed methodology relies on SIR, it opens the door for new regression approaches with a multivariate response.
dc.language.isoen
dc.publisherElsevier
dc.title.enA new sliced inverse regression method for multivariate response
dc.typeArticle de revue
dc.identifier.doi10.1016/j.csda.2014.03.006
dc.subject.halStatistiques [stat]/Méthodologie [stat.ME]
bordeaux.journalComputational Statistics and Data Analysis
bordeaux.page285-299
bordeaux.volume77
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00714981
hal.version1
hal.popularnon
hal.audienceInternationale
dc.subject.itSemiparametric regression model
dc.subject.itDimension reduction
dc.subject.itSliced inverse regression
dc.subject.itMultivariate response
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00714981v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational%20Statistics%20and%20Data%20Analysis&rft.date=2014-09&rft.volume=77&rft.spage=285-299&rft.epage=285-299&rft.eissn=0167-9473&rft.issn=0167-9473&rft.au=COUDRET,%20Rapha%C3%ABl&GIRARD,%20St%C3%A9phane&SARACCO,%20Jerome&rft.genre=article


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée