Variable importance assessment in sliced inverse regression for variable selection

We are interested in treating the relationship between a dependent variable y and a multivariate covariate 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...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2019-01, Vol.48 (1), p.169-199
Main Authors: Jlassi, Ines, Saracco, Jérôme
Format: Article
Language:English
Subjects:
62-07
Computer simulation
Dependent variables
Dimension reduction
Regression analysis
Regression models
Semiparametric regression model
Sliced inverse regression
Statistics
Variable importance
Variable selection
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Description
Summary:We are interested in treating the relationship between a dependent variable y and a multivariate covariate 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 methods allows to reduce the space of the covariate x by estimating the directions β that form an effective dimension reduction (EDR) space. The aim of this article 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. 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.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2017.1375522