S. Arlot, G. Blanchard, and E. Roquain, Some nonasymptotic results on resampling in high dimension, I: Confidence regions, The Annals of Statistics, vol.38, issue.1, pp.51-82, 2010.
DOI : 10.1214/08-AOS667
URL : https://hal.archives-ouvertes.fr/hal-00446194

S. Arlot, G. Blanchard, and E. Roquain, Some nonasymptotic results on resampling in high dimension, II: Multiple tests, The Annals of Statistics, vol.38, issue.1, pp.83-99, 2010.
DOI : 10.1214/08-AOS668
URL : https://hal.archives-ouvertes.fr/hal-00446197

Y. Baraud, S. Huet, and B. Laurent, Adaptive tests of linear hypotheses by model selection, Ann. Statist, vol.31, issue.1, pp.225-251, 2003.

Y. Benjamini and R. Heller, False Discovery Rates for Spatial Signals, Journal of the American Statistical Association, vol.102, issue.480, pp.1272-1281, 2007.
DOI : 10.1198/016214507000000941

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Statist. Soc. Ser. B, vol.57, issue.1, pp.289-300, 1995.

Y. Benjamini and Y. Hochberg, On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics, Journal of Educational and Behavioral Statistics, vol.25, issue.1, pp.60-83, 2000.
DOI : 10.3102/10769986025001060

Y. Benjamini, A. M. Krieger, and D. Yekutieli, Adaptive linear step-up procedures that control the false discovery rate, Biometrika, vol.93, issue.3, pp.491-507, 2006.
DOI : 10.1093/biomet/93.3.491

Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, Ann. Statist, vol.29, issue.4, pp.1165-1188, 2001.

M. A. Black, A note on the adaptive control of false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.25, issue.2, pp.297-304, 2004.
DOI : 10.1073/pnas.1530509100

G. Blanchard, Contrôle non-asymptotique adaptatif du family-wise error rate en tests multiples, Talk at Journées Statistiques du Sud, Porquerolles, 2009.

G. Blanchard and F. Fleuret, Occam???s Hammer, Learning theory, pp.112-126, 2007.
DOI : 10.1007/978-3-540-72927-3_10

G. Blanchard and E. Roquain, Two simple sufficient conditions for FDR control, Electronic Journal of Statistics, vol.2, issue.0, pp.963-992, 2008.
DOI : 10.1214/08-EJS180
URL : https://hal.archives-ouvertes.fr/hal-00354980

G. Blanchard and E. Roquain, Adaptive false discovery rate control under independence and dependence, J. Mach. Learn. Res, vol.10, pp.2837-2871, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00446200

A. Celisse and S. Robin, A cross-validation based estimation of the proportion of true null hypotheses, Journal of Statistical Planning and Inference, vol.140, issue.11, pp.3132-3147, 2010.
DOI : 10.1016/j.jspi.2010.04.014
URL : https://hal.archives-ouvertes.fr/hal-01197599

Z. Chi and Z. Tan, Positive false discovery proportions: intrinsic bounds and adaptive control, Statist. Sinica, vol.18, issue.3, pp.837-860, 2008.

S. Delattre and E. Roquain, On the false discovery proportion convergence under Gaussian equi-correlation, Statistics & Probability Letters, vol.81, issue.1, pp.111-115, 2011.
DOI : 10.1016/j.spl.2010.09.025
URL : https://hal.archives-ouvertes.fr/hal-00497134

S. Dudoit and M. J. Van-der-laan, Multiple testing procedures with applications to genomics, 2008.
DOI : 10.1007/978-0-387-49317-6

C. Durot and Y. Rozenholc, An adaptive test for zero mean, Math. Methods Statist, vol.15, issue.1, pp.26-60, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00748951

B. Efron, Microarrays, Empirical Bayes and the Two-Groups Model, Statistical Science, vol.23, issue.1, pp.1-22, 2008.
DOI : 10.1214/07-STS236

B. Efron, -Values and the Accuracy of Large-Scale Statistical Estimates, Journal of the American Statistical Association, vol.105, issue.491, pp.1042-1055, 2010.
DOI : 10.1198/jasa.2010.tm09129
URL : https://hal.archives-ouvertes.fr/in2p3-00356269

A. Farcomeni, Some results on the control of the false discovery rate under dependence. Scand, J. Statist, vol.34, issue.2, pp.275-297, 2007.

J. A. Ferreira and A. H. Zwinderman, On the Benjamini???Hochberg method, The Annals of Statistics, vol.34, issue.4, pp.1827-1849, 2006.
DOI : 10.1214/009053606000000425

H. Finner, R. Dickhaus, and M. Roters, On the false discovery rate and an asymptotically optimal rejection curve, The Annals of Statistics, vol.37, issue.2, pp.596-618, 2009.
DOI : 10.1214/07-AOS569

H. Finner, T. Dickhaus, and M. Roters, Dependency and false discovery rate: Asymptotics, The Annals of Statistics, vol.35, issue.4, pp.1432-1455, 2007.
DOI : 10.1214/009053607000000046
URL : http://arxiv.org/abs/0710.3171

R. A. Fisher, Design of Experiments, BMJ, vol.1, issue.3923, 1935.
DOI : 10.1136/bmj.1.3923.554-a

Y. Gavrilov, Y. Benjamini, and S. K. Sarkar, An adaptive step-down procedure with proven FDR control under independence, The Annals of Statistics, vol.37, issue.2, pp.619-629, 2009.
DOI : 10.1214/07-AOS586

C. Genovese and L. Wasserman, Operating characteristics and extensions of the false discovery rate procedure, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.64, issue.3, pp.499-517, 2002.
DOI : 10.1111/1467-9868.00347

C. Genovese and L. Wasserman, A stochastic process approach to false discovery control Soumis au Journal de la Société Française de Statistique File: Roquain-jsfds-version2.tex, compiled with jsfds, version, Ann. Statist, vol.12, issue.3, pp.321035-1061, 2004.

J. Goeman and L. Finos, The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses, Statistical Applications in Genetics and Molecular Biology, vol.11, issue.1, 2010.
DOI : 10.1515/1544-6115.1554

J. Goeman and A. Solari, The sequential rejection principle of familywise error control, The Annals of Statistics, vol.38, issue.6, pp.3782-3810, 2010.
DOI : 10.1214/10-AOS829

W. Guo and M. B. Rao, On control of the false discovery rate under no assumption of dependency, Journal of Statistical Planning and Inference, vol.138, issue.10, pp.3176-3188, 2008.
DOI : 10.1016/j.jspi.2008.01.003

Y. Hochberg and A. C. Tamhane, Multiple comparison procedures Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics, 1987.

S. Holm, A simple sequentially rejective multiple test procedure. Scand, J. Statist, vol.6, issue.2, pp.65-70, 1979.

K. I. Kim, E. Roquain, and M. A. Van, Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing, Statistical Applications in Genetics and Molecular Biology, vol.9, issue.1, 2010.
DOI : 10.2202/1544-6115.1532
URL : https://hal.archives-ouvertes.fr/hal-00559782

K. I. Kim and M. Van, Effects of dependence in high-dimensional multiple testing problems, BMC Bioinformatics, vol.9, issue.1, p.114, 2008.
DOI : 10.1186/1471-2105-9-114

E. L. Lehmann and J. P. Romano, Generalizations of the familywise error rate, The Annals of Statistics, vol.33, issue.3, pp.1138-1154, 2005.
DOI : 10.1214/009053605000000084

E. L. Lehmann and J. P. Romano, Testing statistical hypotheses. Springer Texts in Statistics, 2005.

P. Massart, Concentration inequalities and model selection Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, Lecture Notes in Mathematics, vol.1896, 2003.

N. Meinshausen, L. Meier, and P. Bühlmann, -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

C. J. Miller, C. Genovese, R. C. Nichol, L. Wasserman, A. Connolly et al., Controlling the False-Discovery Rate in Astrophysical Data Analysis, The Astronomical Journal, vol.122, issue.6, pp.1223492-3505, 2001.
DOI : 10.1086/324109

D. Pantazis, T. E. Nichols, S. Baillet, and R. M. Leahy, A comparison of random field theory and permutation methods for the statistical analysis of MEG data, NeuroImage, vol.25, issue.2, pp.383-394, 2005.
DOI : 10.1016/j.neuroimage.2004.09.040

J. P. Romano and A. M. Shaikh, Stepup procedures for control of generalizations of the familywise error rate, The Annals of Statistics, vol.34, issue.4, pp.1850-1873, 2006.
DOI : 10.1214/009053606000000461

J. P. Romano and M. Wolf, Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing, Journal of the American Statistical Association, vol.100, issue.469, pp.94-108, 2005.
DOI : 10.1198/016214504000000539
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.694.8480

J. P. Romano and M. Wolf, Control of generalized error rates in multiple testing, The Annals of Statistics, vol.35, issue.4, pp.1378-1408, 2007.
DOI : 10.1214/009053606000001622

J. P. Romano and M. Wolf, Balanced control of generalized error rates, The Annals of Statistics, vol.38, issue.1, pp.598-633, 2010.
DOI : 10.1214/09-AOS734

E. Roquain and M. Van, Optimal weighting for false discovery rate control, Electronic Journal of Statistics, vol.3, issue.0, pp.678-711, 2009.
DOI : 10.1214/09-EJS430
URL : https://hal.archives-ouvertes.fr/hal-00446198

E. Roquain and F. Villers, Exact calculations for false discovery proportion with application to least favorable configurations, The Annals of Statistics, vol.39, issue.1, pp.584-612, 2011.
DOI : 10.1214/10-AOS847SUPP
URL : https://hal.archives-ouvertes.fr/hal-00456385

D. Rubin, S. Dudoit, and M. Van-der-laan, A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting, Statistical Applications in Genetics and Molecular Biology, vol.5, issue.1, p.pp, 2006.
DOI : 10.2202/1544-6115.1148

S. K. Sarkar, procedures, The Annals of Statistics, vol.30, issue.1, pp.239-257, 2002.
DOI : 10.1214/aos/1015362192

S. K. Sarkar, On methods controlling the false discovery rate, Sankhya, Ser. A, vol.70, pp.135-168, 2008.

T. Schweder and E. Spjøtvoll, -values to evaluate many tests simultaneously, Biometrika, vol.69, issue.3, pp.493-502, 1982.
DOI : 10.1093/biomet/69.3.493
URL : https://hal.archives-ouvertes.fr/hal-00764552

P. Seeger, A Note on a Method for the Analysis of Significances en masse, Technometrics, vol.11, issue.3, pp.586-593, 1968.
DOI : 10.1080/00401706.1968.10490605

V. G. Spokoiny, Adaptive hypothesis testing using wavelets, The Annals of Statistics, vol.24, issue.6, pp.2477-2498, 1996.
DOI : 10.1214/aos/1032181163
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.323.8383

J. D. Storey, A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.82, issue.3, pp.479-498, 2002.
DOI : 10.1111/1467-9868.00346

J. D. Storey, J. E. Taylor, and D. Siegmund, Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach File: Roquain-jsfds-version2.tex, compiled with jsfds, version, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.12, issue.1, pp.66187-205, 2004.

M. A. Van-de-wiel, J. Berkhof, and W. N. Van-wieringen, Testing the prediction error difference between 2 predictors, Biostatistics, vol.10, issue.3, pp.550-560, 2009.
DOI : 10.1093/biostatistics/kxp011

M. A. Van-de-wiel and W. N. Van-wieringen, CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss, Cancer Inform, vol.3, pp.55-63, 2007.

M. J. Van-der-laan, M. D. Birkner, and A. E. Hubbard, Empirical Bayes and Resampling Based Multiple Testing Procedure Controlling Tail Probability of the Proportion of False Positives., :Art. 29, 32 pp. (electronic), 2005.
DOI : 10.2202/1544-6115.1143

N. Verzelen and F. Villers, Goodness-of-fit tests for high-dimensional Gaussian linear models, The Annals of Statistics, vol.38, issue.2, pp.704-752, 2010.
DOI : 10.1214/08-AOS629
URL : https://hal.archives-ouvertes.fr/inria-00186919

L. Wasserman, All of statistics Springer Texts in Statistics, 2004.

L. Wasserman and K. Roeder, Weighted hypothesis testing, Dept. of statistics, 2006.

P. H. Westfall and S. S. Young, Resampling-Based Multiple Testing Examples and Methods for P-Value Adjustment, 1993.