[FGS08] Inverse Regression Methods Based on Fuzzy Partitions

Revue Internationale avec comité de lecture : Journal Int. J. of Pure and Applied Mathematics, vol. 43, pp. 43-62, 2008
Résumé: We consider a semiparametric regression model such that the dependent variable y is linked to some indices xŒB_k through an unknown link function. Li (1991) introduced sliced inverse regression methods (SIR-I, SIR-II and SIRa) in order to estimate the effective dimension reduction space spanned by the vectors Bk. These methods computationally fast and simple but are influenced by the choice of slices in the estimation process. In this paper, we suggest to use versions of SIR methods based on fuzzy clusters instead of slices which can be seen as hard clusters and we exhibit the corresponding algorithm. We illustrate the sample behaviour of the fuzzy inverse regression estimators and compare them with the SIR ones on simulation study.

Equipe: msdma


@article {
title="{Inverse Regression Methods Based on Fuzzy Partitions}",
author="S. Ferrigno and A. Gannoun and J. Saracco",
journal="Int. J. of Pure and Applied Mathematics",