S. Allassonnì-ere, E. Kuhn, and A. Trouvé, Construction of Bayesian deformable models via a stochastic approximation algorithm: A convergence study, Bernoulli, vol.16, issue.3, pp.641-678, 2010.
DOI : 10.3150/09-BEJ229

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the em algorithm. Annals of statistics pp, pp.94-128, 1999.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the royal statistical society. Series B, pp.1-38, 1977.

P. Diggle, P. Heagerty, K. Y. Liang, and S. Zeger, Analysis of Longitudinal Data., Biometrics, vol.53, issue.2, 2002.
DOI : 10.2307/2533983

M. C. Donohue, H. Jacqmin-gadda, L. Goff, M. Thomas, R. G. Raman et al., Estimating long-term multivariate progression from short-term data, Alzheimer's & Dementia, vol.10, issue.5, pp.400-410, 2014.
DOI : 10.1016/j.jalz.2013.10.003

S. Durrleman, X. Pennec, A. Trouvé, J. Braga, G. Gerig et al., Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data, International Journal of Computer Vision, vol.31, issue.3, pp.22-59, 2013.
DOI : 10.1007/s11263-012-0592-x
URL : https://hal.archives-ouvertes.fr/hal-00813825

H. M. Fonteijn, M. Modat, M. J. Clarkson, J. Barnes, M. Lehmann et al., An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease, NeuroImage, vol.60, issue.3, pp.1880-1889, 2012.
DOI : 10.1016/j.neuroimage.2012.01.062

C. R. Jack, D. S. Knopman, W. J. Jagust, L. M. Shaw, P. S. Aisen et al., Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade, The Lancet Neurology, vol.9, issue.1, pp.119-128, 2010.
DOI : 10.1016/S1474-4422(09)70299-6

E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.
DOI : 10.1016/j.csda.2004.07.002

N. M. Laird and J. H. Ware, Random-Effects Models for Longitudinal Data, Biometrics, vol.38, issue.4, pp.963-974, 1982.
DOI : 10.2307/2529876

J. B. Schiratti, S. Allassonnì-ere, A. Routier, A. The, O. Colliot et al., A mixed-effcts model with time reparametrization for longitudinal univariate manifold-valued data, Information Processing in Medical Imaging, pp.564-576, 2015.

J. D. Singer and J. B. Willett, Applied longitudinal data analysis: Modeling change and event occurrence, 2003.
DOI : 10.1093/acprof:oso/9780195152968.001.0001

N. Singh, J. Hinkle, S. Joshi, and P. T. Fletcher, A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis, Information Processing in Medical Imaging, pp.560-571, 2013.
DOI : 10.1007/978-3-642-38868-2_47

J. Su, S. Kurtek, E. Klassen, and A. Srivastava, Statistical analysis of trajectories on Riemannian manifolds: Bird migration, hurricane tracking and video surveillance, The Annals of Applied Statistics, vol.8, issue.1, pp.530-552, 2014.
DOI : 10.1214/13-AOAS701