JLASSI, Ines; SARACCO, Jerome

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JLASSI, Ines
Faculté des Sciences de Monastir [FSM]

SARACCO, Jerome
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

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We 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.< Leer menos

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Variable importance assessment in sliced inverse regression for variable selection

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