An introduction to SIR: A statistical method for dimension reduction in multivariate regression

Stephane Girard 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. It however requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possibleand a regularization technique has to be used. We propose to introduce a Gaussian prior distribution on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors are proposed leading to new regularizations of the SIR method. A comparison on simulated data as well asan application to the estimation of Mars surface physical properties from hyperspectral images are provided.
Type de document :
Pré-publication, Document de travail
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Contributeur : Stephane Girard <>
Soumis le : mercredi 27 août 2014 - 17:30:15
Dernière modification le : mercredi 11 avril 2018 - 01:58:44
Document(s) archivé(s) le : vendredi 28 novembre 2014 - 10:51:30


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  • HAL Id : hal-01058721, version 1



Stephane Girard. An introduction to SIR: A statistical method for dimension reduction in multivariate regression. 2014. 〈hal-01058721〉



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