Open Access
2011 The Smooth-Lasso and other 1+2-penalized methods
Mohamed Hebiri, Sara van de Geer
Electron. J. Statist. 5: 1184-1226 (2011). DOI: 10.1214/11-EJS638


We consider a linear regression problem in a high dimensional setting where the number of covariates p can be much larger than the sample size n. In such a situation, one often assumes sparsity of the regression vector, i.e., the regression vector contains many zero components. We propose a Lasso-type estimator β̂Quad (where ‘Quad’ stands for quadratic) which is based on two penalty terms. The first one is the 1 norm of the regression coefficients used to exploit the sparsity of the regression as done by the Lasso estimator, whereas the second is a quadratic penalty term introduced to capture some additional information on the setting of the problem. We detail two special cases: the Elastic-Net β̂EN introduced in [42], which deals with sparse problems where correlations between variables may exist; and the Smooth-Lasso β̂SL, which responds to sparse problems where successive regression coefficients are known to vary slowly (in some situations, this can also be interpreted in terms of correlations between successive variables). From a theoretical point of view, we establish variable selection consistency results and show that β̂Quad achieves a Sparsity Inequality, i.e., a bound in terms of the number of non-zero components of the ‘true’ regression vector. These results are provided under a weaker assumption on the Gram matrix than the one used by the Lasso. In some situations this guarantees a significant improvement over the Lasso. Furthermore, a simulation study is conducted and shows that the S-Lasso β̂SL performs better than known methods as the Lasso, the Elastic-Net β̂EN, and the Fused-Lasso (introduced in [30]) with respect to the estimation accuracy. This is especially the case when the regression vector is ‘smooth’, i.e., when the variations between successive coefficients of the unknown parameter of the regression are small. The study also reveals that the theoretical calibration of the tuning parameters and the one based on 10 fold cross validation imply two S-Lasso solutions with close performance.


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Mohamed Hebiri. Sara van de Geer. "The Smooth-Lasso and other 1+2-penalized methods." Electron. J. Statist. 5 1184 - 1226, 2011.


Published: 2011
First available in Project Euclid: 19 October 2011

MathSciNet: MR2842904
Digital Object Identifier: 10.1214/11-EJS638

Primary: 62J05 , 62J07
Secondary: 62F12 , 62H20

Keywords: elastic-net , High-dimensional data , LARS , Lasso , restricted eigenvalues , Sparsity , Variable selection

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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