Student Sliced Inverse Regression
Résumé
Sliced Inverse Regression (SIR) has been extensively used to reduce the dimension of the predictor space before performing regression. Recently it has been shown that this techniques is, not surprisingly, sensitive to noise. Different approaches has been proposed to robustify SIR, in this paper we start considering an inverse problem proposed by R.D. Cook and we show that the framework can be extended to take into account a non-Gaussian noise. Generalized Student distribution are considered and all parameters are estimated via EM algorithm. The algorithm is outlined and tested comparing the results with different approaches on simulated data. Results on a real dataset are provided showing the interest of this technique in presence of outliers.
Domaines
Statistiques [math.ST]
Fichier principal
robustsir-soumis.pdf (389.54 Ko)
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StudentSIR.zip (3.58 Ko)
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Origine | Fichiers produits par l'(les) auteur(s) |
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Format | Autre |
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Origine | Fichiers produits par l'(les) auteur(s) |
Commentaire | This archive contains the Matlab code associated with the simulation section of the paper. Simulations.m is the main file, sir2.m is the function for standard SIR and stSirI.m is the function for Student SIR. |