Advanced topics in Sliced Inverse Regression V2

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 fe...

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Bibliographic Details
Published in:Journal of multivariate analysis 2022, Vol.188
Main Authors: Saracco, Jérôme, Lorenzo, Hadrien, Girard, Stéphane
Format: Article
Language:English
Subjects:
Statistics
Online Access:Get full text
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Summary: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.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2021.104852