D. K. Andrews, Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models, Econometrica, vol.59, issue.2, pp.307-345, 1991.
DOI : 10.2307/2938259

P. J. Bickel and Y. Ritov, Nonparametric estimators which can be plugged-in, Annals of Statistics, vol.7, issue.4 8, p.314, 2003.

S. C. Brenner and L. R. Scott, The Mathematical Theory of Finite Element Methods, 2008.

N. Brunel, Parameter estimation of ODE???s via nonparametric estimators, Electronic Journal of 11 Statistics, pp.1242-1267, 2008.
DOI : 10.1214/07-EJS132

O. Cappé, E. Moulines, and T. Rydén, Inference in Hidden Markov Models, p.13, 2005.

L. Goldstein and K. Messer, Optimal Plug-in Estimators for Nonparametric Functional Estimation, The Annals of Statistics, vol.20, issue.3
DOI : 10.1214/aos/1176348770

S. Gugushvili, C. A. Klaassen, A. R. Hall, E. Ionides, A. Bhadra et al., Root-n-consistent parameter estimation for systems of ordinary 19 differential equations: bypassing numerical integration via smoothing. Bernoulli, to appear Generalized Method of Moments, Annals of Statistics, vol.20, issue.1 2, pp.391776-1802, 2011.

E. L. Ionides, C. Breto, and A. A. King, Inference for nonlinear dynamical systems, Proceedings of 3
DOI : 10.1073/pnas.0603181103

H. Liang and H. Wu, Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models, Journal of the American Statistical Association, vol.103, issue.484, pp.1031570-1583, 2007.
DOI : 10.1198/016214508000000797

J. Madar, J. Abonyi, H. Roubos, F. Szeifert, ]. H. Miao et al., Incorporating prior knowledge in cubic spline ap- 8 proximation -application to the identification of reaction kinetic models. Industrial and Engineering 9 On identifiability of nonlinear ode models and 11 applications in viral dynamics Parameter estimation in biochemical pathways: a com- 13 parison of global optimization methods Convergence rates and asymptotic normality for series estimators Existence theory for nonlinear ordinary differential equations Mathematics and its 17 applications. Kluwer Parameter estimation 19 in continuous-time dynamic models using principal differential analysis, Journal of 15 Econometrics Computers and Chemical 20 Engineering [20] M. Quach, N. Brunel, and F. d'Alche Buc. Estimating parameters and hidden variables in non-linear 1, pp.4043-4049, 1997.

J. O. Ramsay, G. Hooker, J. Cao, and D. Campbell, Parameter estimation for differential equations: a generalized smoothing approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.284, issue.5, pp.741-796, 2007.
DOI : 10.1111/j.1467-9868.2007.00610.x

D. Ruppert, M. P. Wand, R. J. Carroll, and S. Van-de-geer, Semiparametric regression Cambridge series on statis- 7 tical and probabilistic mathematics Empirical processes in M-estimation 9 [24] A.W. van der Vaart Asymptotic Statistics. Cambridge Series in Statistical and Probabilities 10 Mathematics A spline least squares method for numerical parameter estimation in differential 12 equations Decoupling dynamical systems for pathway identification from metabolic 14 profiles, Xue, H. Miao, and H. Wu. Sieve estimation of constant and time-varying coefficients in nonlinear 16 ordinary differential equation models by considering both numerical error and measurement error. 17 Annals of Statistics, pp.28-46, 1982.