SARACCO, Jerome; JLASSI, Ines

Métadonnées

Licence d’utilisation du document

SARACCO, Jerome
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]

JLASSI, Ines
Faculté des Sciences de Monastir [FSM]

Langue

Communication dans un congrès

Ce document a été publié dans

9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016), 2016-12-09, Séville.

Résumé en anglais

The focus is on treating the relationship between a dependent variable $y$ and a $p$-dimensional covariate $x$ in a semiparametric regression model. Since the purpose of most social, biological or environmental science research is the explanation, the determination of the importance of the variables is a major concern. It is a way to determine which variables are the most important when predicting $y$. Sliced inverse regression (SIR) methods allows us to reduce the space of the covariate $x$ by estimating the directions that form an effective dimension reduction (EDR) space. The aim is to propose a computational method based on importance variable measure (only relying on the EDR space) in order to select the most useful variables in SIR model. The numerical behavior of this approach, implemented in R, is studied on a simulation study. An illustration on a real data is also provided.< Réduire

Métadonnées

Partager cette publication !

Licence d’utilisation du document

Variable importance assessment in sliced inverse regression for variable selection

Langue

Ce document a été publié dans

Résumé en anglais

URI

Origine

Unités de recherche