The BerHu penalty and the grouped effect

Understand

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.
• In the case of small sample size and large covariables numbers, this penalty is not very satisfactory variable selection method.
• In this paper, we introduce an adaptive reversed version of Huber's criterion as a penalty function.