L. Birgé and P. Massart, Minimum Contrast Estimators on Sieves: Exponential Bounds and Rates of Convergence, Bernoulli, vol.4, issue.3, pp.329-375, 1998.
DOI : 10.2307/3318720

F. Comte, Y. Rozenholc, and M. Taupin, Penalized contrast estimator for adaptive density deconvolution, Canadian Journal of Statistics, vol.18, issue.3, pp.431-452, 2006.
DOI : 10.1002/cjs.5550340305
URL : https://hal.archives-ouvertes.fr/hal-00016489

F. Comte and A. Samson, Nonparametric estimation of random-effects densities in linear mixed-effects model, Journal of Nonparametric Statistics, vol.20, issue.4, pp.951-975, 2012.
DOI : 10.1111/j.0006-341X.2001.00795.x
URL : https://hal.archives-ouvertes.fr/hal-00657052

M. Davidian and D. M. Giltinan, Nonlinear Models for Repeated Measurement Data, 1995.

M. Delattre, V. Genon-catalot, and A. Samson, Maximum Likelihood Estimation for Stochastic Differential Equations with Random Effects, Scandinavian Journal of Statistics, vol.80, issue.2, 2012.
DOI : 10.1111/j.1467-9469.2012.00813.x
URL : https://hal.archives-ouvertes.fr/hal-00650844

S. Ditlevsen, D. Gaetano, and A. , Mixed effects in stochastic differential equation models, pp.137-153, 2005.

S. Donnet and A. Samson, Parametric inference for mixed models defined by stochastic differential equations, ESAIM: Probability and Statistics, vol.12, pp.196-218, 2008.
DOI : 10.1051/ps:2007045
URL : https://hal.archives-ouvertes.fr/hal-00263515

S. Donnet and A. Samson, EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00519576

A. Goldenshluger and O. Lepski, Bandwidth selection in kernel density estimation: Oracle inequalities and adaptive minimax optimality, The Annals of Statistics, vol.39, issue.3, pp.1608-1632, 2011.
DOI : 10.1214/11-AOS883
URL : https://hal.archives-ouvertes.fr/hal-01265258

N. Ikeda and S. Watanabe, Stochastic differential equations and diffusion processes, 1989.

I. Karatzas and S. Shreve, Brownian motion and stochastic calculus, 1997.

T. Klein and E. Rio, Concentration around the mean for maxima of empirical processes, The Annals of Probability, vol.33, issue.3, pp.1060-1077, 2005.
DOI : 10.1214/009117905000000044

L. Nie, Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear and Nonlinear Mixed-Effects Models, Metrika, vol.86, issue.2, pp.123-143, 2006.
DOI : 10.1007/s00184-005-0001-3

L. Nie, Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications, Journal of Statistical Planning and Inference, vol.137, issue.6, pp.1787-1804, 2007.
DOI : 10.1016/j.jspi.2005.06.010

L. Nie and M. Yang, Strong consistency of MLE in nonlinear mixed-effects models with large cluster size, Sankhya, vol.67, pp.736-763, 2005.

R. Overgaard, N. Jonsson, C. Tornøe, and H. Madsen, Non-Linear Mixed-Effects Models with Stochastic Differential Equations: Implementation of an Estimation Algorithm, Journal of Pharmacokinetics and Pharmacodynamics, vol.29, issue.5???6, pp.85-107, 2005.
DOI : 10.1007/s10928-005-2104-x

U. Picchini, D. Gaetano, A. Ditlevsen, and S. , Stochastic differential mixed-effects models. Scand, J. Statist, vol.37, pp.67-90, 2010.

U. Picchini and S. Ditlevsen, Practical estimation of high dimensional stochastic differential mixed-effects models, Computational Statistics & Data Analysis, vol.55, issue.3, pp.1426-1444, 2011.
DOI : 10.1016/j.csda.2010.10.003

J. C. Pinheiro and D. M. Bates, Mixed-Effects Models in S and S-PLUS, 2000.
DOI : 10.1007/978-1-4419-0318-1

D. Revuz and M. Yor, Continuous martingales and Brownian motion, Fundamental Principles of Mathematical Sciences], vol.293, 1999.

A. B. Tsybakov, Introduction to nonparametric estimation. Revised and extended from the 2004 French original. Translated by Vladimir Zaiats, 2009.