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Advanced topics in Sliced Inverse Regression

Stéphane Girard 1 Hadrien Lorenzo 2 Jérôme Saracco 2 
2 ASTRAL - Méthodes avancées d’apprentissage statistique et de contrôle
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, Bordeaux INP - Institut Polytechnique de Bordeaux, Naval Group
Abstract : Since its introduction in the early 90's, 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.
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Submitted on : Wednesday, October 6, 2021 - 1:59:20 PM
Last modification on : Sunday, June 26, 2022 - 3:14:59 AM
Long-term archiving on: : Friday, January 7, 2022 - 6:57:28 PM

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Stéphane Girard, Hadrien Lorenzo, Jérôme Saracco. Advanced topics in Sliced Inverse Regression. Journal of Multivariate Analysis, Elsevier, 2022, 188, pp.104852. ⟨10.1016/j.jmva.2021.104852⟩. ⟨hal-03367798⟩

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