GIRARD, Stéphane; LORENZO, Hadrien; SARACCO, Jérôme

doi:10.1016/j.jmva.2021.104852

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GIRARD, Stéphane
Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension [STATIFY]

LORENZO, Hadrien
Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]

SARACCO, Jérôme
Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]

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Journal of Multivariate Analysis. 2022, vol. 188, p. 104852

Elsevier

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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.< Leer menos

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Curse of dimensionality

Multivariate response

Regularization

Semi-parametric regression model

Sufficient dimension reduction

Variable selection

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Advanced topics in Sliced Inverse Regression

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