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hal.structure.identifierFaculté des Sciences de Monastir [FSM]
dc.contributor.authorJLASSI, Ines
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorSARACCO, Jerome
dc.date.accessioned2024-04-04T03:12:11Z
dc.date.available2024-04-04T03:12:11Z
dc.date.created2016
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193843
dc.description.abstractEnWe are interested in treating the relationship between a dependentvariable $y$ and a multivariate covariate $x \in {\R}^p$ in asemiparametric regression model. Since the purpose of most social,biological or environmental science research is the explanation, the determination of theimportance of the variables is a major concern. It is a way todetermine which variables are the most important when predicting$y$. Sliced inverse regression methods allows to reduce the space of thecovariate $x$ by estimating the directions $\beta$ that form aneffective dimension reduction (EDR) space. The aim of this paper isto propose a computational method based on importance variable measure (only relying on the EDR space) in order to select the most useful variables. The numerical behavior of this new method, implemented in R, is studied on a simulation study. An illustration on a real data is also provided.
dc.language.isoen
dc.title.enVariable importance assessment in sliced inverse regression for variable selection
dc.typeDocument de travail - Pré-publication
dc.subject.halStatistiques [stat]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-01417552
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01417552v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=JLASSI,%20Ines&SARACCO,%20Jerome&rft.genre=preprint


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