B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of Statistics, vol.32, pp.407-499, 2004.

C. Biernacki, Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures for Grouped Data and Behaviour of the EM Algorithm, Scandinavian Journal of Statistics, vol.27, issue.3, pp.569-586, 2007.
DOI : 10.2307/2285455

C. Stein, Estimation of the Mean of a Multivariate Normal Distribution, The Annals of Statistics, vol.9, issue.6, pp.1135-1151, 1981.
DOI : 10.1214/aos/1176345632

H. D. Bondell and B. J. Reich, Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR, Biometrics, vol.67, issue.1, pp.115-123, 2008.
DOI : 10.1111/j.1541-0420.2007.00843.x

D. B. Sharma, H. D. Bondell, and H. H. Zhang, Consistent Group Identification and Variable Selection in Regression With Correlated Predictors, Journal of Computational and Graphical Statistics, vol.37, issue.2, 2013.
DOI : 10.1080/15533174.2012.707849

A. E. Hoerl and W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.24, issue.1, pp.55-67, 1970.
DOI : 10.2307/1909769

G. Casella, An Introduction to Empirical Bayes Data Analysis. The American Statistician, pp.83-87, 1985.

G. Celeux, D. Chauveau, and J. Diebolt, Stochastic versions of the em algorithm: an experimental study in the mixture case, Journal of Statistical Computation and Simulation, vol.67, issue.4, pp.287-314, 1996.
DOI : 10.1214/aos/1176346060

URL : https://hal.archives-ouvertes.fr/hal-00693519

G. Policello, Conditional Maximum Likelihood Estimation in Gaussian Mixtures, Statistical Distributions in Scientific Work, pp.111-125, 1981.
DOI : 10.1007/978-94-009-8552-0_9

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

C. G. Wei and M. A. Tanner, A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, vol.51, issue.411, pp.699-704, 1990.
DOI : 10.1214/aos/1176346060

H. Chun and S. Keles, Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression, Genetics, vol.182, issue.1, pp.79-90, 2009.
DOI : 10.1534/genetics.109.100362

H. Ishwaran and J. Rao, Spike and slab variable selection: Frequentist and Bayesian strategies, The Annals of Statistics, vol.33, issue.2, pp.730-773, 2005.
DOI : 10.1214/009053604000001147

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998

H. Zou, T. Hastie, and R. Tibshirani, On the ???degrees of freedom??? of the lasso, The Annals of Statistics, vol.35, issue.5, pp.2173-2192, 2007.
DOI : 10.1214/009053607000000127

Z. J. Daye and X. J. Jeng, Shrinkage and model selection with correlated variables via weighted fusion, Computational Statistics & Data Analysis, vol.53, issue.4, pp.1284-1298, 2009.
DOI : 10.1016/j.csda.2008.11.007

A. P. Dempster, M. N. Laird, and R. D. , Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.39, pp.1-22, 1977.

R. Tibshirani, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994.

S. Petrone, J. Rousseau, and C. Scricciolo, Bayes and empirical Bayes: do they merge? arXiv:1204, 2012.
DOI : 10.1093/biomet/ast067

URL : http://arxiv.org/abs/1204.1470

S. Petry and G. Tutz, Shrinkage and variable selection by polytopes, Journal of Statistical Planning and Inference, vol.142, issue.1, 2009.
DOI : 10.1016/j.jspi.2011.06.020

T. Hastie, R. Tibshirani, and F. J. , The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations, 2001.

T. J. Mitchell and J. J. Beauchamp, Bayesian Variable Selection in Linear Regression, Journal of the American Statistical Association, vol.51, issue.404, pp.1023-1032, 1988.
DOI : 10.1080/01621459.1982.10477809

X. Shen and H. Huang, Grouping Pursuit Through a Regularization Solution Surface, Journal of the American Statistical Association, vol.105, issue.490, pp.727-739, 2010.
DOI : 10.1198/jasa.2010.tm09380

M. Y. Park, T. Hastie, and R. Tibshirani, Averaged gene expressions for regression, Biostatistics, vol.8, issue.2, pp.212-227, 2007.
DOI : 10.1093/biostatistics/kxl002

Y. She and S. University, Sparse regression with exact clustering, Electronic Journal of Statistics, vol.4, issue.0, 2008.
DOI : 10.1214/10-EJS578

URL : http://projecteuclid.org/download/pdfview_1/euclid.ejs/1286889184