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

H. Braak and E. Braak, Staging of alzheimer's disease-related neurofibrillary changes, Neurobiology of Aging, vol.16, issue.3, pp.271-278, 1995.
DOI : 10.1016/0197-4580(95)00021-6

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

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169767

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

M. Girolami and B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011.
DOI : 10.1111/j.1467-9868.2010.00765.x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.580

M. W. Hirsch, Differential topology, 2012.
DOI : 10.1007/978-1-4684-9449-5

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. 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