Open Access
Issue
ESAIM: PS
Volume 25, 2021
Page(s) 408 - 432
DOI https://doi.org/10.1051/ps/2021015
Published online 05 October 2021
  1. O. Ajmal, L. Duchateau and E. Kuhn, Convergent stochastic algorithm for parameter estimation in frailty models using integrated partial likelihood. Preprint arXiv:1909.07056 (2019). [Google Scholar]
  2. S. Allassonnière and J. Chevallier, A new class of em algorithms. escaping local minima and handling intractable sampling. Preprint (2019). [Google Scholar]
  3. S. Allassonnière, E. Kuhn, A. Trouvé, et al., Construction of Bayesian deformable models via a stochastic approximation algorithm: a convergence study. Bernoulli 16 (2010) 641–678. [Google Scholar]
  4. S. Allassonniere, L. Younes, et al., A stochastic algorithm for probabilistic independent component analysis. Ann. Appl. Stat. 6 (2012) 125–160. [Google Scholar]
  5. C. Andrieu, É. Moulines and P. Priouret, Stability of stochastic approximation under verifiable conditions. SIAM J. Control Optim. 44 (2005) 283–312. [Google Scholar]
  6. S. Balakrishnan, M.J. Wainwright, B. Yu, et al., Statistical guarantees for the em algorithm: from population to sample-based analysis. Ann. Stat. 45 (2017) 77–120. [Google Scholar]
  7. S. Benzekry, C. Lamont, A. Beheshti, A. Tracz, J.M. Ebos, L. Hlatky and P. Hahnfeldt, Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol. 10 (2014) e1003800. [CrossRef] [PubMed] [Google Scholar]
  8. A. Bône, O. Colliot and S. Durrleman, Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018) 9271–9280. [Google Scholar]
  9. S. Chrétien and A.O. Hero, On em algorithms and their proximal generalizations. ESAIM: Probab. Stat. 12 (2008) 308–326. [Google Scholar]
  10. V. Debavelaere, S. Durrleman and S. Allassonnière, Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories. Int. J. Comput. Vision (2020) 1–16. [Google Scholar]
  11. B. Delyon, M. Lavielle and E. Moulines, Convergence of a stochastic approximation version of the em algorithm. Ann. Stat. (1999) 94–128. [Google Scholar]
  12. A.P. Dempster, N.M. Laird and D.B. Rubin, Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc. Ser. B (Methodological) 39 (1977) 1–22. [Google Scholar]
  13. A. Dubois, M. Lavielle, S. Gsteiger, E. Pigeolet and F. Mentré, Model-based analyses of bioequivalence crossover trials using the stochastic approximation expectation maximisation algorithm. Stat. Med. 30 (2011) 2582–2600. [CrossRef] [PubMed] [Google Scholar]
  14. J. Guedj and A.S. Perelson, Second-phase hepatitis c virus RNA decline during Telaprevir-based therapy increases with drug effectiveness: implications for treatment duration. Hepatology 53 (2011) 1801–1808. [CrossRef] [PubMed] [Google Scholar]
  15. E. Kuhn and M. Lavielle, Coupling a stochastic approximation version of em with an MCMC procedure. ESAIM: Probab. Stat. 8 (2004) 115–131. [Google Scholar]
  16. E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models. Comput. Stat. Data Anal. 49 (2005) 1020–1038. [Google Scholar]
  17. E. Kuhn, C. Matias and T. Rebafka, Properties of the stochastic approximation em algorithm with mini-batch sampling. Stat. Comput. 30 (2020) 1725–1739. [Google Scholar]
  18. T. Lartigue, S. Durrleman and S. Allassonnière, Deterministic approximate em algorithm; application to the Riemann approximation em and the tempered em. Preprint arXiv:2003.10126 (2020). [Google Scholar]
  19. M. Lavielle, Mixed effects models for the population approach: models, tasks, methods and tools. CRC Press (2014). [Google Scholar]
  20. M. Lavielle and F. Mentré, Estimation of population pharmacokinetic parameters of saquinavir in HIV patients with the Monolix software. J. Pharmacokinet. Pharmacodyn. 34 (2007) 229–249. [CrossRef] [PubMed] [Google Scholar]
  21. F. Lindsten, An efficient stochastic approximation em algorithm using conditional particle filters, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE (2013) 6274–6278. [Google Scholar]
  22. Lixoft SAS, Monolix (2020). [Google Scholar]
  23. J. Ma, L. Xu and M.I. Jordan, Asymptotic convergence rate of the algorithm for Gaussian mixtures. Neural Comput. 12 (2000) 2881–2907. [CrossRef] [PubMed] [Google Scholar]
  24. X.-L. Meng et al., On the rate of convergence of the ECM algorithm. Ann. Stat. 22 (1994) 326–339. [Google Scholar]
  25. C. Meza, F. Osorio and R. De la Cruz, Estimation in nonlinear mixed-effects models using heavy-tailed distributions. Stat. Comput. 22 (2012) 121–139. [Google Scholar]
  26. X. Panhard and A. Samson, Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects. Biostatistics 10 (2009) 121–135. [CrossRef] [PubMed] [Google Scholar]
  27. R.A. Redner and H.F. Walker, Mixture densities, maximum likelihood and the algorithm. SIAM Rev. 26 (1984) 195–239. [Google Scholar]
  28. A. Samson, M. Lavielle and F. Mentré, Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: application to HIV dynamics model. Comput. Stat. Data Anal. 51 (2006) 1562–1574. [Google Scholar]
  29. J.-B. Schiratti, S. Allassonniere, O. Colliot and S. Durrleman, Learning spatiotemporal trajectories from manifold-valued longitudinal data. Adv. Neural Inf. Process. Syst. (2015) 2404–2412. [Google Scholar]
  30. D. Sissoko, C. Laouenan, E. Folkesson, A.-B. M’lebing, A.-H. Beavogui, S. Baize, A.-M. Camara, P. Maes, S. Shepherd, C. Danel, et al., Experimental treatment with Favipiravir for Ebola virus disease (the JIKI trial): a historically controlled, single-arm proof-of-concept trial in guinea. PLoS Med. 13 (2016) e1001967. [CrossRef] [PubMed] [Google Scholar]
  31. P. Tseng, An analysis of the algorithm and entropy-like proximal point methods. Math Oper. Res. 29 (2004) 27–44. [Google Scholar]
  32. J. Wang, Em algorithms for nonlinear mixed effects models. Comput. Stat. Data Anal. 51 (2007) 3244–3256. [Google Scholar]
  33. G.C. Wei and M.A. Tanner, A Monte Carlo implementation of the algorithm and the poor man’s data augmentation algorithms. J. Am. Stat. Assoc. 85 (1990) 699–704. [Google Scholar]
  34. C.J. Wu, On the convergence properties of the algorithm. Ann. Stat. (1983) 95–103. [Google Scholar]

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