Advanced topics in Sliced Inverse Regression

Stéphane Girard, Hadrien Lorenzo and Jérôme Saracco

Journal of Multivariate Analysis, 2022, vol. 188, issue C

Abstract: Since its introduction in the early 90s, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex data sets in contexts combining linear dimension reduction with non linear regression. The assumption of dependence of the response variable with respect to only a few linear combinations of the covariates makes it appealing for many computational and real data application aspects. This work proposes an overview of the most active research directions in SIR modeling from multivariate regression models to regularization and variable selection.

Keywords: Curse of dimensionality; Multivariate response; Regularization; Semi-parametric regression model; Sufficient dimension reduction; Variable selection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2021.104852

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