P. Ablin, A. Gramfort, J. Cardoso, and F. Bach, Em algorithms for ica, 2018.

C. Baey, S. Trevezas, and P. Cournède, A non linear mixed effects model of plant growth and estimation via stochastic variants of the em algorithm, Communications in Statistics-Theory and Methods, vol.45, issue.6, pp.1643-1669, 2016.

S. Balakrishnan, M. J. Wainwright, Y. , and B. , Statistical guarantees for the em algorithm: From population to sample-based analysis, Ann. Statist, vol.45, issue.1, pp.77-120, 2017.

D. M. Blei, A. Kucukelbir, and J. D. Mcauliffe, Variational Inference: A Review for Statisticians, Journal of the American statistical Association, vol.112, issue.518, pp.859-877, 2017.

J. G. Booth, J. P. Hobert, J. , and W. , A survey of monte carlo algorithms for maximizing the likelihood of a two-stage hierarchical model, Statistical Modelling, vol.1, issue.4, pp.333-349, 2001.

S. Brooks, A. Gelman, G. L. Jones, and X. Meng, Handbook of Markov chain Monte Carlo. Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 2011.

O. Cappé, Online em algorithm for hidden markov models, Journal of Computational and Graphical Statistics, vol.20, issue.3, pp.728-749, 2011.

O. Cappé and E. Moulines, On-line expectation-maximization algorithm for latent data models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.71, issue.3, pp.593-613, 2009.

A. Chakraborty and K. Das, Inferences for joint modelling of repeated ordinal scores and time to event data. Computational and mathematical methods in medicine, vol.11, pp.281-295, 2010.

K. Chan and J. Ledolter, Monte carlo em estimation for time series models involving counts, Journal of the American Statistical Association, vol.90, issue.429, pp.242-252, 1995.

E. Comets, A. Lavenu, and M. Lavielle, Parameter estimation in nonlinear mixed effect models using saemix, an r implementation of the saem algorithm, Journal of Statistical Software, vol.80, issue.3, pp.1-42, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-01502767

I. Csiszár and G. Tusnády, Information geometry and alternating minimization procedures, Statist. Decisions, pp.205-237, 1984.

A. Defazio, F. R. Bach, S. Lacoste-julien, Z. Ghahramani, M. Welling et al., SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, pp.1646-1654, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01016843

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 (methodological), pp.1-38, 1977.

P. Doukhan, P. Massart, and E. Rio, Invariance principles for absolutely regular empirical processes, Annales de l'IHP Probabilités et statistiques, vol.31, pp.393-427, 1995.

R. A. Fisher, Theory of statistical estimation, vol.22, 1925.

G. Fort and E. Moulines, Convergence of the monte carlo expectation maximization for curved exponential families, The Annals of Statistics, vol.31, issue.4, pp.1220-1259, 2003.

A. Gunawardana and W. Byrne, Convergence theorems for generalized alternating minimization procedures, Journal of Machine Learning Research, vol.6, pp.2049-2073, 2005.

T. Hsiao, P. Khurd, A. Rangarajan, and G. Gindi, An overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental em algorithm, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol.569, issue.2, pp.429-433, 2006.

J. P. Hughes, Mixed effects models with censored data with application to hiv rna levels, Biometrics, vol.55, issue.2, pp.625-629, 1999.

M. Lavielle, Mixed effects models for the population approach: models, tasks, methods and tools, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01122873

R. A. Levine and G. Casella, Implementations of the monte carlo em algorithm, Journal of Computational and Graphical Statistics, vol.10, issue.3, pp.422-439, 2001.

A. Likas and N. Galatsanos, A variational approach for Bayesian blind image deconvolution, IEEE Transactions on signal processing, vol.52, issue.8, pp.2222-2233, 2004.

T. A. Louis, Finding the observed information matrix when using the em algorithm, Journal of the Royal Statistical Society. Series B (Methodological), pp.226-233, 1982.

J. Mairal, Incremental majorization-minimization optimization with application to large-scale machine learning, SIAM Journal on Optimization, vol.25, issue.2, pp.829-855, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00948338

C. E. Mcculloch, Maximum likelihood algorithms for generalized linear mixed models, Journal of the American statistical Association, vol.92, issue.437, pp.162-170, 1997.

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions. Wiley Series in Probability and Statistics, 2008.

S. P. Meyn and R. L. Tweedie, Markov chains and stochastic stability, 2012.

R. Neal and G. Hinton, of NATO advanced science institutes series, series D, Behavioral and Social Sciences, NATO Advanced Study Institute on Learning in Graphical Models, vol.89, pp.355-368, 1996.

R. C. Neath, On convergence properties of the monte carlo em algorithm, Advances in Modern Statistical Theory and Applications: A Festschrift in Honor of Morris L. Eaton, pp.43-62, 2013.

Y. Nesterov, Gradient methods for minimizing composite objective function. CORE Discussion Papers, 2007.

S. Ng and G. Mclachlan, On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures, Statistics and Computing, vol.13, issue.1, pp.45-55, 2003.

S. Ng and G. Mclachlan, Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images, Pattern Recognition, vol.37, issue.8, pp.1573-1589, 2004.

C. P. Robert and G. Casella, , 2005.

N. L. Roux, M. Schmidt, and F. R. Bach, A stochastic gradient method with an exponential convergence rate for finite training sets, Advances in Neural Information Processing Systems, vol.25, pp.2663-2671, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00674995

R. P. Sherman, Y. K. Ho, and S. R. Dalal, Conditions for convergence of monte carlo em sequences with an application to product diffusion modeling, The Econometrics Journal, vol.2, issue.2, pp.248-267, 1999.

B. Thiesson, C. Meek, and D. Heckerman, Accelerating EM for large databases, Machine Learning, vol.45, issue.3, pp.279-299, 2001.

N. Vlassis and A. Likas, A greedy EM algorithm for Gaussian mixture learning, Neural Processing Letters, vol.15, issue.1, pp.77-87, 2002.

Z. Wang, Q. Gu, Y. Ning, and H. Liu, High dimensional expectationmaximization algorithm: Statistical optimization and asymptotic normality, 2014.

G. C. Wei and M. A. Tanner, A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms, Journal of the American Statistical Association, vol.85, issue.411, pp.699-704, 1990.

R. Zhu, L. Wang, C. Zhai, and Q. Gu, High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.4180-4188, 2017.