The adaptive BerHu penalty in robust regression

Sophie Lambert-Lacroix 1 Laurent Zwald 2, 3
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications [Grenoble]
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 SVH - Statistique pour le Vivant et l’Homme
LJK - Laboratoire Jean Kuntzmann
Abstract : We intend to combine Huber's loss with an adaptive reversed version as a penalty function. The purpose is twofold: first we would like to propose an estimator that is robust to data subject to heavy-tailed errors or outliers. Second we hope to overcome the variable selection problem in presence of highly correlated predictors. For instance, in this framework, the adaptive least absolute shrinkage and selection operator (lasso) is not a very satisfactory variable selection method, although it is a popular technique for simultaneous estimation and variable selection. We call this new penalty adaptive BerHu penalty. As for elastic net penalty, small coefficients contribute through their 1 norm to this penalty while larger coefficients cause it to grow quadratically (as ridge regression). We will show that the estimator associated with Huber's loss combined with adaptive BerHu penalty enjoys theoretical properties in the fixed design context. This approach is compared to existing regularization methods such as adaptive elastic net and is illustrated via simulation studies and real data.
Type de document :
Article dans une revue
Journal of Nonparametric Statistics, American Statistical Association, 2016, 28 (3), pp.487 - 514. 〈10.1080/10485252.2016.1190359〉
Liste complète des métadonnées
Contributeur : Laurent Zwald <>
Soumis le : jeudi 27 septembre 2018 - 09:26:35
Dernière modification le : mardi 9 octobre 2018 - 10:13:08


Fichiers produits par l'(les) auteur(s)




Sophie Lambert-Lacroix, Laurent Zwald. The adaptive BerHu penalty in robust regression. Journal of Nonparametric Statistics, American Statistical Association, 2016, 28 (3), pp.487 - 514. 〈10.1080/10485252.2016.1190359〉. 〈hal-01882461〉



Consultations de la notice


Téléchargements de fichiers