A. Arribas-gil, R. De-la-cruz, E. Lebarbier, and C. Meza, Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators, Biometrics, vol.21, issue.2, pp.333-343, 2015.
DOI : 10.1111/biom.12280

G. Celeux, O. Martin, and C. Lavergne, Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments, Statistical Modelling, vol.5, issue.3, pp.243-267, 2005.
DOI : 10.1191/1471082X05st096oa
URL : https://hal.archives-ouvertes.fr/hal-00192771

F. Comte, V. Genon-catalot, and A. Samson, Nonparametric estimation for stochastic differential equations with random effects, Stochastic Processes and their Applications, vol.123, issue.7, 2013.
DOI : 10.1016/j.spa.2013.04.009
URL : https://hal.archives-ouvertes.fr/hal-00761394

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, pp.322-343, 2013.
DOI : 10.1111/j.1467-9469.2012.00813.x
URL : https://hal.archives-ouvertes.fr/hal-00650844

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Jr. R. Stat. Soc. B, vol.39, pp.1-38, 1977.

E. Devijver, Model-based regression clustering for high-dimensional data: application to functional data, Advances in Data Analysis and Classification, vol.21, issue.3, 2014.
DOI : 10.1007/s11634-016-0242-1

C. Dion and V. Genon-catalot, Bidimensional random effect estimation in mixed stochastic differential model, Statistical Inference for Stochastic Processes, vol.47, issue.2, 2015.
DOI : 10.1007/s11203-015-9122-0
URL : https://hal.archives-ouvertes.fr/hal-01103303

S. Ditlevsen, D. Gaetano, and A. , Stochastic vs. deterministic uptake of dodecanedioic acid by isolated rat livers, Bulletin of Mathematical Biology, vol.67, issue.3, pp.547-561, 2005.
DOI : 10.1016/j.bulm.2004.09.005

S. Donnet, J. Foulley, and A. Samson, Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations, Biometrics, vol.10, issue.3, pp.733-741, 2010.
DOI : 10.1111/j.1541-0420.2009.01342.x
URL : https://hal.archives-ouvertes.fr/hal-00360111

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

P. Filipe, C. Braumann, N. Brites, and C. Roquete, Modelling animal growth in random environments: An application using nonparametric estimation, Biometrical Journal, vol.10, issue.5, pp.653-666, 2010.
DOI : 10.1002/bimj.200900273

P. Filipe, C. Braumann, N. Brites, and C. Roquete, Recent development in Modeling and applications in Statistics, 2013.

V. Genon-catalot and C. Larédo, Estimation for stochastic differential equations with mixed effects, Statistics, vol.3, issue.5, 2015.
DOI : 10.1007/s11203-015-9122-0
URL : https://hal.archives-ouvertes.fr/hal-00807258

J. Jacques and C. Preda, Functional data clustering: a survey Advances in Data Analysis and Classification 8, pp.231-255, 2014.

F. Jaffrézic, C. Meza, M. Lavielle, and J. Foulley, Genetic analysis of growth curves using the SAEM algorithm, Genet. Sel. Evol, vol.38, pp.583-600, 2006.

C. Keribin, Consistent estimation of the order of mixture models, Sankhya: The Indian Journal of Statistics, vol.62, pp.49-66, 2000.