Wu, S.; Shen, X.; Geyer, C. J. - In: Biometrika 96 (2009) 3, pp. 513-527
Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L<sub>1</sub>- and L<sub>∞</sub>-norms, that adapts to a...
