Selection bias in gene extraction on the basis of microarray gene-expression data, Proceedings of the National Academy of Sciences, vol.99, issue.10, pp.6562-6566, 2002. ,
DOI : 10.1073/pnas.102102699
Differential expression analysis for sequence count data, Genome Biology, vol.11, issue.10, p.106, 2010. ,
DOI : 10.1186/gb-2010-11-10-r106
Permutation Tests for Linear Models, Australian <html_ent glyph="@amp;" ascii="&"/> New Zealand Journal of Statistics, vol.43, issue.1, pp.75-88, 2001. ,
DOI : 10.1111/1467-842X.00156
Optimization with Sparsity-Inducing Penalties, Foundations and Trends?? in Machine Learning, vol.4, issue.1, pp.1-106, 2012. ,
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00613125
A tutorial on statistical methods for population association studies, Nature Reviews Genetics, vol.5, issue.10, pp.781-791, 2006. ,
DOI : 10.1038/nrg1155
Least squares after model selection in high-dimensional sparse models, Bernoulli, vol.19, issue.2, pp.521-547, 2013. ,
DOI : 10.3150/11-BEJ410SUPP
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. ,
Machine learning versus statistical modeling Statistical significance in high-dimensional linear models, Biometrical Journal Bühlmann P Bernoulli, vol.19, pp.1212-1242, 2013. ,
The Dantzig selector: Statistical estimation when p is much larger than n, The Annals of Statistics, vol.35, issue.6, pp.2313-2351, 2007. ,
DOI : 10.1214/009053606000001523
Rates of convergence of the Adaptive LASSO estimators to the Oracle distribution and higher order refinements by the bootstrap, The Annals of Statistics, vol.41, issue.3, pp.1232-1259, 2013. ,
DOI : 10.1214/13-AOS1106SUPP
Performance of some variable selection methods when multicollinearity is present, Chemometrics and Intelligent Laboratory Systems, vol.78, issue.1-2, pp.103-112, 2005. ,
DOI : 10.1016/j.chemolab.2004.12.011
Significance testing in ridge regression for genetic data, BMC Bioinformatics, vol.12, issue.1, pp.1-15, 2011. ,
DOI : 10.1111/j.1467-9868.2005.00503.x
Distinct Genetic Loci Control Plasma HIV-RNA and Cellular HIV-DNA Levels in HIV-1 Infection: The ANRS Genome Wide Association 01 Study, PLoS ONE, vol.46, issue.12, p.3907, 2008. ,
DOI : 10.1371/journal.pone.0003907.s002
Multiple testing procedures with applications to genomics, 2008. ,
DOI : 10.1007/978-0-387-49317-6
Least angle regression, The Annals of Statistics, vol.32, issue.2, pp.407-499, 2004. ,
Least Absolute Shrinkage is Equivalent to Quadratic Penalization, Perspectives in Neural Computing, vol.1, pp.201-206, 1998. ,
DOI : 10.1007/978-1-4471-1599-1_27
Outcomes of the equivalence of adaptive ridge with least absolute shrinkage, Advances in Neural Information Processing Systems 11, pp.445-451, 1998. ,
Tests of regression coefficients under ridge regression models, Journal of Statistical Computation and Simulation, vol.39, issue.1-4, pp.341-356, 1999. ,
DOI : 10.1016/0304-4076(84)90040-X
Generalized Additive Models, Monographs on Statistics and Applied Probability, 1990. ,
Asymptotic properties of bridge estimators in sparse high-dimensional regression models, The Annals of Statistics, vol.36, issue.2, pp.587-613, 2008. ,
DOI : 10.1214/009053607000000875
Penalized regression, standard errors, and Bayesian lassos, Bayesian Analysis, vol.5, issue.2, pp.369-411, 2010. ,
DOI : 10.1214/10-BA607
Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression, Electronic Journal of Statistics, vol.7, issue.0, pp.3124-3169, 2013. ,
DOI : 10.1214/14-EJS875
A significance test for the lasso, The Annals of Statistics, vol.42, issue.2, pp.413-468, 2014. ,
DOI : 10.1214/14-AOS1175REJ
Relaxed Lasso, Computational Statistics & Data Analysis, vol.52, issue.1, pp.374-393, 2007. ,
DOI : 10.1016/j.csda.2006.12.019
-Values for High-Dimensional Regression, Journal of the American Statistical Association, vol.104, issue.488, pp.1671-1681, 2009. ,
DOI : 10.1198/jasa.2009.tm08647
URL : https://hal.archives-ouvertes.fr/hal-00122771
Variable selection for generalized canonical correlation analysis, Biostatistics, vol.15, issue.3, pp.569-583, 2014. ,
DOI : 10.1093/biostatistics/kxu001
URL : https://hal.archives-ouvertes.fr/hal-01071432
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series BMethodological), vol.58, issue.1, pp.267-288, 1996. ,
Minimax risks for sparse regressions: Ultra-high dimensional phenomenons, Electronic Journal of Statistics, vol.6, issue.0, pp.38-90, 2012. ,
DOI : 10.1214/12-EJS666SUPP
URL : https://hal.archives-ouvertes.fr/hal-00508339
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction, SIAM Journal on Imaging Sciences, vol.1, issue.3, pp.248-272, 2008. ,
DOI : 10.1137/080724265
High-dimensional variable selection, The Annals of Statistics, vol.37, issue.5A, pp.2178-2201, 2009. ,
DOI : 10.1214/08-AOS646
Feature selection for highdimensional genomic microarray data, Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), pp.601-608, 2001. ,
Confidence intervals for low dimensional parameters in high dimensional linear models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.36, issue.1, pp.217-242, 2014. ,
DOI : 10.1111/rssb.12026