Robust Regression through the Huber's criterion and adaptive lasso penalty - Archive ouverte HAL Access content directly
Journal Articles Electronic Journal of Statistics Year : 2011

Robust Regression through the Huber's criterion and adaptive lasso penalty

(1) , (2, 3)
1
2
3

Abstract

The Huber's Criterion is a useful method for robust regression. The adaptive least absolute shrinkage and selection operator (lasso) is a popular technique for simultaneous estimation and variable selection. The adaptive weights in the adaptive lasso allow to have the oracle properties. In this paper we propose to combine the Huber's criterion and adaptive penalty as lasso. This regression technique is resistant to heavy-tailed er- rors or outliers in the response. Furthermore, we show that the estimator associated with this procedure enjoys the oracle properties. This approach is compared with LAD-lasso based on least absolute deviation with adaptive lasso. Extensive simulation studies demonstrate satisfactory finite-sample performance of such procedure. A real example is analyzed for illustration purposes.
Fichier principal
Vignette du fichier
EJS635.pdf (476.08 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00661864 , version 1 (20-01-2012)

Identifiers

Cite

Sophie Lambert-Lacroix, Laurent Zwald. Robust Regression through the Huber's criterion and adaptive lasso penalty. Electronic Journal of Statistics , 2011, 5, pp.1015-1053. ⟨10.1214/11-EJS635⟩. ⟨hal-00661864⟩
1200 View
1294 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More