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.

K. Bertin, C. Lacour, and V. Rivoirard, Adaptive pointwise estimation of conditional density function, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00922555

K. Bertin, C. Lacour, and V. Rivoirard, Adaptive pointwise estimation of conditional density function, Ann. Inst. H. Poincar Probab. Statist, vol.52, issue.2, pp.939-980, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00922555

C. Butucea, Two adaptive rates of convergence in pointwise density estimation, Math. Methods Statist, vol.9, issue.1, pp.39-64, 2000.

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.
URL : https://hal.archives-ouvertes.fr/hal-01197599

G. Chagny, Penalization versus goldenshluger-lepski strategies in warped bases regression, ESAIM: Probability and Statistics, vol.17, pp.328-358, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02132877

M. Chichignoud, T. Van-ha-hoang, V. Ngoc, and . Rivoirard, Adaptive wavelet multivariate regression with errors in variables, Electronic journal of statistics, vol.11, issue.1, pp.682-724, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01253508

F. Comte, S. Gaïffas, and A. Guilloux, Adaptive estimation of the conditional intensity of markerdependent counting processes, Ann. Inst. Henri Poincaré Probab. Stat, vol.47, issue.4, pp.1171-1196, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00333356

F. Comte and C. Lacour, Anisotropic adaptive kernel deconvolution, Ann. Inst. Henri Poincaré Probab. Stat, vol.49, issue.2, pp.569-609, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00579608

F. Comte, Estimation non-paramétrique, Spartacus-IDH, 2015.

F. Comte, V. Genon-catalot, and A. Samson, Nonparametric estimation for stochastic differential equations with random effects, Stochastic Processes and their Applications, vol.123, pp.2522-2551, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00761394

F. Comte and T. Rebafka, Nonparametric weighted estimators for biased data, Journal of Statistical Planning and Inference, vol.174, pp.104-128, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01101970

M. Doumic, M. Hoffmann, P. Reynaud-bouret, and V. Rivoirard, Nonparametric estimation of the division rate of a size-structured population, SIAM Journal on Numerical Analysis, vol.50, issue.2, pp.925-950, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00578694

B. Efron, R. Tibshirani, D. John, V. Storey, and . Tusher, Empirical bayes analysis of a microarray experiment, Journal of the American statistical association, vol.96, issue.456, pp.1151-1160, 2001.

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.

E. Giné and R. Nickl, An exponential inequality for the distribution function of the kernel density estimator, with applications to adaptive estimation, Probab. Theory Related Fields, vol.143, issue.3-4, pp.569-596, 2009.

A. Goldenshluger and O. Lepski, Bandwidth selection in kernel density estimation: orcale inequalities and adaptive minimax optimality, The Annals of Statistics, vol.39, issue.3, pp.1608-1632, 2011.

J. Peter and . Huber, A robust version of the probability ratio test, The Annals of Mathematical Statistics, pp.1753-1758, 1965.

I. A. Ibragimov and R. Z. , Has 1 minski?. An estimate of the density of a distribution, Zap. Nauchn. Sem. Leningrad. Otdel. Mat. Inst. Steklov. (LOMI), vol.98, pp.161-162, 1980.

M. Langaas, B. H. Lindqvist, and E. Ferkingstad, Estimating the proportion of true null hypotheses, with application to dna microarray data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.4, pp.555-572, 2005.

O. Lepski, Multivariate density estimation under sup-norm loss: oracle approach, adaptation and independence structure, The Annals of Statistics, vol.41, issue.2, pp.1005-1034, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01265250

H. Liu and C. Gao, Density estimation with contaminated data: Minimax rates and theory of adaptation, 2017.

C. Van-hanh-nguyen and . Matias, Nonparametric estimation of the density of the alternative hypothesis in a multiple testing setup. application to local false discovery rate estimation, ESAIM: Probability and Statistics, vol.18, p.584612, 2014.

C. Van-hanh-nguyen and . Matias, On efficient estimators of the proportion of true null hypotheses in a multiple testing setup, Scandinavian Journal of Statistics, vol.41, issue.4, pp.1167-1194, 2014.

P. Reynaud, -. Bouret, V. Rivoirard, F. Grammont, and C. Tuleau-malot, Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis, The Journal of Mathematical Neuroscience, vol.4, issue.1, p.1, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01100718

A. Stéphane-robin, J. Bar-hen, L. Daudin, and . Pierre, A semi-parametric approach for mixture models: Application to local false discovery rate estimation, Computational Statistics & Data Analysis, vol.51, issue.12, pp.5483-5493, 2007.

F. Eugene and . Schuster, Incorporating support constraints into nonparametric estimators of densities, Communications in Statistics-Theory and methods, vol.14, issue.5, pp.1123-1136, 1985.

D. John and . Storey, A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.64, issue.3, pp.479-498, 2002.

K. Strimmer, A unified approach to false discovery rate estimation, BMC bioinformatics, vol.9, issue.1, p.303, 2008.

B. Alexandre and . Tsybakov, Introduction to Nonparametric Estimation. Springer series in Statistics, 2009.