H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

C. Arlot, S. Arlot, and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054

URL : https://hal.archives-ouvertes.fr/hal-00407906

. Barber, Controlling the false discovery rate via knockoffs, The Annals of Statistics, vol.43, issue.5, pp.2055-2085, 2015.
DOI : 10.1214/15-AOS1337SUPP

. Benjamini, . Hochberg, Y. Benjamini, and Y. Hochberg, Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, issue.1, pp.289-300, 1995.

. Bühlmann, Correlated variables in regression: Clustering and sparse estimation, Journal of Statistical Planning and Inference, vol.143, issue.11, pp.1835-3871, 2013.
DOI : 10.1016/j.jspi.2013.05.019

. Bühlmann, P. Van-de-geer-]-bühlmann, and S. Van-de-geer, Statistics for High-Dimensional Data: Methods, Theory and Applications, 2011.
DOI : 10.1007/978-3-642-20192-9

O. J. Dunn, Estimation of the Medians for Dependent Variables, The Annals of Mathematical Statistics, vol.30, issue.1, pp.192-197, 1959.
DOI : 10.1214/aoms/1177706374

. Fan, Variance estimation using refitted cross-validation in ultrahigh dimensional regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.36, issue.1, pp.37-65, 2012.
DOI : 10.1214/009053607000000802

, Tuning parameter selection in high dimensional penalized likelihood, Journal of the Royal Statistical Society: Series B (Statistical Methodology), issue.3, pp.75531-552, 2013.

. Giraud, Gaussian Model Selection with Unknown Variance, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00123420

J. , Group Lasso with Overlap and Graph Lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.433-440, 2009.

. Jain, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.31264-323, 1999.
DOI : 10.1145/331499.331504

. Jamshidian, A study of partial F tests for multiple linear regression models, Computational Statistics & Data Analysis, vol.51, issue.12, pp.516269-6284, 2007.
DOI : 10.1016/j.csda.2007.01.015

. Jenatton, Structured variable selection with sparsity-inducing norms, J. Mach. Learn. Res, vol.12, pp.2777-2824, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00377732

H. Liu and J. Zhang, Estimation consistency of the group lasso and its applications, JMLR, 2009.

. Ma, Supervised group Lasso with applications to microarray data analysis, BMC Bioinformatics, vol.8, issue.1, p.60, 2007.
DOI : 10.1186/1471-2105-8-60

N. Meinshausen, Hierarchical testing of variable importance, Biometrika, vol.95, issue.2, pp.265-278, 2008.
DOI : 10.1093/biomet/asn007

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1186/1471-2105-9-307

. Park, Averaged gene expressions for regression, Biostatistics, vol.67, issue.24, pp.212-227, 2007.
DOI : 10.1111/j.1467-9868.2005.00503.x

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

. Simon, . Tibshirani, N. Simon, and R. Tibshirani, Standardization and the Group Lasso Penalty, Statistica Sinica, vol.22, issue.3, 2011.
DOI : 10.5705/ss.2011.075

URL : http://europepmc.org/articles/pmc4527185?pdf=render

. Sun, Consistent Selection of Tuning Parameters via Variable Selection Stability, pp.3419-3440, 2013.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994.
DOI : 10.1111/j.1467-9868.2011.00771.x

. Tibshirani, , 2005.

, Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society Series B, pp.91-108

M. J. Wainwright, Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso), IEEE Transactions on Information Theory, vol.55, issue.5, pp.2183-2202, 2009.
DOI : 10.1109/TIT.2009.2016018

R. Wasserman, L. Wasserman, and K. Roeder, High-dimensional variable selection, The Annals of Statistics, vol.37, issue.5A, pp.2178-2201, 2009.
DOI : 10.1214/08-AOS646

. Witten, The Cluster Elastic Net for High-Dimensional Regression With Unknown Variable Grouping, Technometrics, vol.58, issue.1, pp.112-122, 2014.
DOI : 10.1111/j.1467-9868.2005.00503.x

H. Zou, A fast unified algorithm for solving grouplasso penalized learning problems, Statistics and Computing, vol.25, issue.6, pp.1129-1141, 2015.

L. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

Y. Zhao, P. Zhao, and B. Yu, On Model Selection Consistency of Lasso, J. Mach. Learn. Res, vol.7, pp.2541-2563, 2006.