A new method for estimation and model selection: ρ-estimation

Abstract : The aim of this paper is to present a new estimation procedure that can be applied in various statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal) estimators. In density estimation, they asymptotically coincide with the celebrated maximum likelihood estimators at least when the statistical model is regular enough and contains the true density to estimate. For very general models of densities, including non-compact ones, these estimators are robust with respect to the Hellinger distance and converge at optimal rate (up to a possible logarithmic factor) in all cases we know. In the regression setting, our approach improves upon the classical least squares in many respects. In simple linear regression for example, it provides an estimation of the coefficients that are both robust to outliers and simultaneously rate- optimal (or nearly rate-optimal) for a large class of error distributions including Gaussian, Laplace, Cauchy and uniform among others.
Type de document :
Article dans une revue
Inventiones Mathematicae, Springer Verlag, 2016
Liste complète des métadonnées

Contributeur : Yannick Baraud <>
Soumis le : vendredi 3 juin 2016 - 12:18:40
Dernière modification le : jeudi 21 mars 2019 - 13:11:23

Lien texte intégral


  • HAL Id : hal-01326224, version 1
  • ARXIV : 1403.6057


Yannick Baraud, Lucien Birgé, Mathieu Sart. A new method for estimation and model selection: ρ-estimation. Inventiones Mathematicae, Springer Verlag, 2016. 〈hal-01326224〉



Consultations de la notice