L. Birgé and P. Massart, From Model Selection to Adaptive Estimation, pp.55-87, 1997.
DOI : 10.1007/978-1-4612-1880-7_4

C. Butucea, Deconvolution of supersmooth densities with smooth noise. Canad, J. Statist, vol.32, issue.2, pp.181-192, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00103058

C. Butucea and F. Comte, Adaptive estimation of linear functionals in the convolution model and applications, Bernoulli, vol.15, issue.1, pp.2007-2011, 2007.
DOI : 10.3150/08-BEJ146
URL : https://hal.archives-ouvertes.fr/hal-00133750

C. Butucea, C. Matias, and C. Pouet, Adaptivity in convolution models with partially known noise distribution, Electronic Journal of Statistics, vol.2, issue.0, pp.897-915, 2008.
DOI : 10.1214/08-EJS225
URL : https://hal.archives-ouvertes.fr/hal-00592227

C. Butucea and A. B. Tsybakov, Sharp optimality in density deconvolution with dominating bias. I, Teoriya Veroyatnostei i ee Primeneniya, vol.52, issue.1, pp.111-128, 2007.
DOI : 10.4213/tvp7

R. J. Carroll and P. Hall, Optimal Rates of Convergence for Deconvolving a Density, Journal of the American Statistical Association, vol.74, issue.404, pp.1184-1186, 1988.
DOI : 10.1080/01621459.1988.10478718

E. A. Cator, Deconvolution with arbitrarily smooth kernels, Statistics & Probability Letters, vol.54, issue.2, pp.205-214, 2001.
DOI : 10.1016/S0167-7152(01)00083-9

L. Cavalier and M. Raimondo, Wavelet deconvolution with noisy eigen-values, IEEE Trans. Signal Process, 2009.
DOI : 10.1109/tsp.2007.893754

F. Comte, Y. Rozenholc, and M. Taupin, Penalized contrast estimator for adaptive density deconvolution. Canad, J. Statist, vol.34, issue.3, pp.431-452, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00016489

F. Comte, Y. Rozenholc, and M. Taupin, Finite sample penalization in adaptive density deconvolution, Journal of Statistical Computation and Simulation, vol.1, issue.11, pp.77977-1000, 2007.
DOI : 10.2307/3316026
URL : https://hal.archives-ouvertes.fr/hal-00016503

A. Delaigle and I. Gijbels, Bootstrap bandwidth selection in kernel density estimation from a contaminated sample, Annals of the Institute of Statistical Mathematics, vol.52, issue.1, pp.19-47, 2004.
DOI : 10.1007/BF02530523

L. Devroye, Consistent deconvolution in density estimation. Canad, J. Statist, vol.17, issue.2, pp.235-239, 1989.

P. J. Diggle and P. Hall, A Fourier approach to nonparametric deconvolution of a density estimate, J. Roy. Statist. Soc. Ser. B, vol.55, issue.2, pp.523-531, 1993.

S. Efromovich, Density Estimation for the Case of Supersmooth Measurement Error, Journal of the American Statistical Association, vol.23, issue.438, pp.526-535, 1997.
DOI : 10.1080/01621459.1997.10474005

J. Fan, On the Optimal Rates of Convergence for Nonparametric Deconvolution Problems, The Annals of Statistics, vol.19, issue.3, pp.1257-1272, 1991.
DOI : 10.1214/aos/1176348248

J. Fan and J. Koo, Wavelet deconvolution, IEEE Trans. Inform. Theory, vol.48, issue.3, pp.734-747, 2002.

C. H. Hesse, Data-driven deconvolution, Journal of Nonparametric Statistics, vol.18, issue.4, pp.343-373, 1999.
DOI : 10.1080/10485259908832766

J. Johannes, Deconvolution with unknown error distribution, The Annals of Statistics, vol.37, issue.5A, 2007.
DOI : 10.1214/08-AOS652
URL : http://arxiv.org/abs/0705.3482

J. Koo, Logspline deconvolution in Besov space. Scand, J. Statist, vol.26, issue.1, pp.73-86, 1999.

C. Lacour, Rates of convergence for nonparametric deconvolution, Comptes Rendus Mathematique, vol.342, issue.11, pp.342877-882, 2006.
DOI : 10.1016/j.crma.2006.04.006
URL : https://hal.archives-ouvertes.fr/hal-00115610

M. C. Liu and R. L. Taylor, A consistent nonparametric density estimator for the deconvolution problem, Canadian Journal of Statistics, vol.59, issue.11, pp.427-438, 1989.
DOI : 10.2307/3315482

E. Masry, Multivariate probability density deconvolution for stationary random processes, IEEE Transactions on Information Theory, vol.37, issue.4, pp.1105-1115, 1991.
DOI : 10.1109/18.87002

A. Meister, On the effect of misspecifying the error density in a deconvolution problem, Canadian Journal of Statistics, vol.21, issue.4, pp.439-449, 2004.
DOI : 10.2307/3316026

M. H. Neumann, On the effect of estimating the error density in nonparametric deconvolution, Journal of Nonparametric Statistics, vol.46, issue.4, pp.307-330, 1997.
DOI : 10.1214/aos/1176348768

M. Pensky and B. Vidakovic, Adaptive wavelet estimator for nonparametric density deconvolution, Ann. Statist, vol.27, issue.6, pp.2033-2053, 1999.

L. Stefanski and R. J. Carroll, Deconvolving kernel density estimators, Statistics, vol.48, issue.2, pp.169-184, 1990.
DOI : 10.1109/TIT.1977.1055802

M. Talagrand, New concentration inequalities in product spaces, Inventiones Mathematicae, vol.126, issue.3, pp.505-563, 1996.
DOI : 10.1007/s002220050108

C. Zhang, Fourier Methods for Estimating Mixing Densities and Distributions, The Annals of Statistics, vol.18, issue.2, pp.806-831, 1990.
DOI : 10.1214/aos/1176347627
URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176347627