]. P. Andersen and R. Gill, Cox's Regression Model for Counting Processes: A Large Sample Study, The Annals of Statistics, vol.10, issue.4, pp.1100-1120, 1982.
DOI : 10.1214/aos/1176345976

H. Attouch, Variational Convergence of Functions and Operators, p.773850, 1984.

Z. Bai, C. Rao, and Y. Wu, M -estimation of multivariate linear regression parameters under a convex discrepancy function, Statistica Sinica, vol.2, issue.1, pp.237-254, 1992.

J. Fan and R. Li, Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1438-1360, 2001.
DOI : 10.1198/016214501753382273
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.4174

I. Gannaz, Estimation par ondelettes dans les modeles partiellement linéaires, 2007.

C. J. Geyer, On the Asymptotics of Constrained $M$-Estimation, The Annals of Statistics, vol.22, issue.4, pp.1993-2010, 1994.
DOI : 10.1214/aos/1176325768

M. Grant and S. Boyd, Cvx: Matlab software for disciplined convex programming (web page and software), 2009.

M. Grant and S. Boyd, Graph implementations for nonsmooth convex programs, recent advances in learning and control (a tribute to m. vidyasagar), v. blondel, s. boyd, and h. kimura, lecture notes in control and information sciences, pp.95-110, 2008.

L. Gyorfi, M. Kohler, A. Krzy?, and H. Walk, A distributionfree theory of nonparametric regression Springer Series in Statistics Two robust alternatives to leastsquares regression, J. Am. Stat. Assoc, vol.647, issue.72, pp.1920390828-833, 1977.

J. Hiriart-urruty and C. Lemaréchal, Convex analysis and minimization algorithms I . Grundlehren der Mathematischen Wissenschaften, p.1261420, 1991.

P. Huber, Robust Statistics, p.606374, 1981.
DOI : 10.1002/0471725250

R. Jennrich, Asymptotic Properties of Non-Linear Least Squares Estimators, The Annals of Mathematical Statistics, vol.40, issue.2, pp.633-643, 1969.
DOI : 10.1214/aoms/1177697731

T. Kim and C. Muller, Two-stage Huber estimation, MR2298946 1052 S. Lambert-Lacroix and L. Zwald [15] K. Knight. Epi-convergence in distribution and stochastic equisemicontinuity Corpus-based work, pp.405-418, 1997.
DOI : 10.1016/j.jspi.2006.01.007
URL : http://www.ivie.es/downloads/docs/wpasad/wpasad-2005-17.pdf

K. Knight and W. Fu, Asymptotics for Lasso-type estimators, Ann. Stat, vol.28, issue.5, pp.1356-1378, 2000.

R. Koenker, Quantile regression Econometric Society Monographs 38, p.2268657, 2005.

C. Leng, Y. Lin, and G. Wahba, A note on the Lasso and related procedures in model selection, Stat. Sin, vol.16, issue.4, pp.1273-1284, 2006.

L. Mclinden and R. C. Bergstrom, Preservation of convergence of convex sets and functions in finite dimensions, Transactions of the American Mathematical Society, vol.268, issue.1, pp.127-142, 1981.
DOI : 10.1090/S0002-9947-1981-0628449-5

N. Meinshausen and P. Buhlmann, 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

M. Osborne, B. Presnell, and B. Turlach, On the lasso and its dual, journal of Computational and Graphical Statistics, vol.9, pp.319-337, 2000.

A. B. Owen, A robust hybrid of lasso and ridge regression, 2006.
DOI : 10.1090/conm/443/08555

G. C. Pflug, Asymptotic dominance and confidence for solutions of stochastic programs, Czech. J. Oper. Res, vol.1, issue.1, pp.21-30, 1992.

D. Pollard, Asymptotics for Least Absolute Deviation Regression Estimators, Econometric Theory, vol.1, issue.02, pp.186-199, 1991.
DOI : 10.1080/02331888808802084

W. J. Rey, Introduction to robust and quasi-robust statistical methods, Universitext. Berlin etc, 1983.
DOI : 10.1007/978-3-642-69389-2

R. Rockafellar, Convex analysis, Princeton Landmarks in Mathematics, p.1451876, 1970.
DOI : 10.1515/9781400873173

R. Rockafellar and R. J. Wets, Variational analysis. Grundlehren der Mathematischen Wissenschaften, p.1491362, 1998.

P. J. Rousseeuw and C. Croux, Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association, vol.69, issue.424, pp.1273-1283, 1993.
DOI : 10.1080/01621459.1984.10477105

S. Sardy, P. Tseng, and A. Bruce, Robust wavelet denoising, Robust wavelet denoising. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, pp.1146-1152, 2001.
DOI : 10.1109/78.923297

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

J. F. Sturm, Using SeDuMi 1. 02, a MATLAB toolbox for optimization over symmetric cones, p.1778433, 1999.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1996.

A. Van and . Vaart, Asymptotic statistics. Cambridge Series in Statistical and Probabilistic Mathematics, 3. Cambridge, p.1652247, 1998.

A. Van-der-vaart and J. A. Wellner, Weak convergence and empirical processes. With applications to statistics, p.1385671, 1996.

H. Wang and C. Leng, Unified LASSO Estimation by Least Squares Approximation, Journal of the American Statistical Association, vol.102, issue.479, pp.1039-1048, 2007.
DOI : 10.1198/016214507000000509

H. Wang, G. Li, and G. Jiang, Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso, Journal of Business & Economic Statistics, vol.25, issue.3, pp.347-355, 2007.
DOI : 10.1198/073500106000000251

H. Wang, R. Li, and C. Tsai, Tuning parameter selectors for the smoothly clipped absolute deviation method, Biometrika, vol.94, issue.3, pp.553-568, 2007.
DOI : 10.1093/biomet/asm053

P. Zhao and B. Yu, On Model Selection Consistency of Lasso, p.2274449, 2006.

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735