Skip to Main content Skip to Navigation
Journal articles

Student Sliced Inverse Regression

Alessandro Chiancone 1, 2, 3 Florence Forbes 2 Stéphane Girard 2
1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
GIPSA-DIS - Département Images et Signal, GIPSA-PSD - GIPSA Pôle Sciences des Données
2 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) has been extensively used to reduce the dimension of the predictor space before performing regression. SIR is originally a model free method but it has been shown to actually correspond to the maximum likelihood of an inverse regression model with Gaussian errors. This intrinsic Gaussianity of standard SIR may explain its high sensitivity to outliers as observed in a number of studies. To improve robustness, the inverse regression formulation of SIR is therefore extended to non-Gaussian errors with heavy-tailed distributions. Considering Student distributed errors it is shown that the inverse regression remains tractable via an Expectation- Maximization (EM) algorithm. The algorithm is outlined and tested in the presence of outliers, both in simulated and real data, showing improved results in comparison to a number of other existing approaches.
Complete list of metadatas

Cited literature [33 references]  Display  Hide  Download
Contributor : Alessandro Chiancone <>
Submitted on : Monday, August 8, 2016 - 10:10:57 AM
Last modification on : Wednesday, May 13, 2020 - 4:30:04 PM
Document(s) archivé(s) le : Wednesday, November 9, 2016 - 11:46:45 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Alessandro Chiancone, Florence Forbes, Stéphane Girard. Student Sliced Inverse Regression. Computational Statistics and Data Analysis, Elsevier, 2017, 113, pp.441-456. ⟨10.1016/j.csda.2016.08.004⟩. ⟨hal-01294982v3⟩



Record views


Files downloads