P. [. Alquier, V. Belloni, and . Chernozhukov, Lasso, iterative feature selection and the correlation selector: Oracle inequalities and numerical performances High dimensional sparse econometric models: An introduction, Inverse Problems and High-Dimensional Estimation . Springer Lecture Notes in Statistics, pp.1129-1152, 2008.

Y. [. Bickel, A. Ritov, and . Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, pp.1705-1732, 2009.
DOI : 10.1214/08-AOS620
URL : https://hal.archives-ouvertes.fr/hal-00401585

]. F. Btw07a, A. Bunea, M. Tsybakov, and . Wegkamp, Aggregation for Gaussian regression, Ann. Statist, vol.35, issue.4, pp.1674-1697, 2007.

]. F. Btw07b, A. Bunea, M. Tsybakov, and . Wegkamp, Sparsity oracle inequalities for the lasso, Electron. J. Stat, vol.1, pp.169-194, 2007.

]. F. Bun08 and . Bunea, Consistent selection via the Lasso for high dimensional approximating regression models [Cav11] L. Cavalier Inverse problems in statistics, Inverse Problems and High-Dimensional Estimation. Springer Lecture Notes in Statistics, 2008.

M. [. Chesneau and . Hebiri, Some theoretical results on the Grouped Variables Lasso, Mathematical Methods of Statistics, vol.17, issue.4, pp.317-326, 2008.
DOI : 10.3103/S1066530708040030
URL : https://hal.archives-ouvertes.fr/hal-00145160

T. [. Candes and . Tao, The dantzig selector: statistical estimation when p is much larger than n, Ann. Statist, vol.35, 2007.

A. [. Dalalyan and . Tsybakov, Aggregation by Exponential Weighting and Sharp Oracle Inequalities, Proceedings. Lecture Notes in Computer Science, vol.4539, pp.97-111, 2007.
DOI : 10.1007/978-3-540-72927-3_9
URL : https://hal.archives-ouvertes.fr/hal-00160857

]. M. Heb09 and . Hebiri, Quelques questions de sélection de variables autour de l'estimateur LASSO, 2009.

V. Koltchinskii, The Dantzig selector and sparsity oracle inequalities, Bernoulli, vol.15, issue.3, pp.799-828, 2009.
DOI : 10.3150/09-BEJ187
URL : http://arxiv.org/abs/0909.0861

]. V. Kol09b and . Koltchinskii, Sparse recovery in convex hulls via entropy penalization, Ann. Statist, vol.37, issue.3, pp.1332-1359, 2009.

]. K. Lou08 and . Lounici, Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators, Electron. J. Stat, vol.2, pp.90-102, 2008.

P. [. Meinshausen and . Bühlmann, High-dimensional graphs and variable selection with the Lasso, The Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.
DOI : 10.1214/009053606000000281

]. L. Mvdgb09, S. Meier, P. Van-de-geer, and . Bühlmann, High-dimensional additive modeling, Ann. Statist, vol.37, issue.6B, pp.3779-3821, 2009.

B. [. Meinshausen and . Yu, Lasso-type recovery of sparse representations for high-dimensional data Presnell, and B. Turlach. On the LASSO and its dual, Ann. Statist. J. Comput. Graph. Statist, vol.37, issue.92, pp.246-270319, 2000.

R. Tibshirani, J. Royvdg08, ]. S. Van-de-geer, S. Van-de-geer, and P. Bühlmann, High-dimensional generalized linear models and the lasso On the conditions used to prove oracle results for the lasso Sharp thresholds for noisy and high-dimensional recovery of sparsity using l1-constrained quadratic programming On model selection consistency of Lasso, Statist. Soc. Ser. B Ann. Statist. Elect. Journ. Statist. J. Mach. Learn. Res, vol.58, issue.7, pp.267-288614, 1996.