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MIA Paris

Maud Delattre

Maître de Conférence

tel : 01 44 08 17 47
UMR518 AgroParisTech/INRA
Département MMIP
16 rue Claude Bernard
75 231 Paris Cedex 05

Research Interests

  • Models: Mixed Models, Markov models and hidden Markov models, Stochastic Differential Equations, segmentation models
  • Mathematic statistics: Asymptotic properties of estimators, Model selection
  • Computational statistics: stochastic algorithms (SAEM algorithm, MCMC algorithms), dynamic programming
  • Applications: pharmacology, genomics, environment, epidemiology


-> ProdINRA

-> Hal

  1. Delattre, M., Genon-Catalot, V. and Larédo, C. (2018) Approximate maximum likelihood estimation for stochastic differential equations with random effects in the drift and the diffusion. Metrika 81 (8) p. 953-983 (link)
  2. Delattre, M., Genon-Catalot, V. and Larédo, C. (2017) Parametric inference for discrete observations of diffusion processes with mixed effects. Stochastic Processes and their Applications 128(6) p. 1929-1957 (link)
  3. Brault, V., Delattre, M., Lebarbier, E., Mary-Huard, T. and Lévy-Leduc, C. (2017)  Estimating the number of change-points in a two-dimensional segmentation model without penalization. Scandinavian Journal of Statistics 44(2) p. 563-580 (link) 
  4. Colin, P., Delattre, M., Mancini, P. and Micallef, S. (2017) An Escalation for Bivariate Binary Endpoints Controlling the Risk of Overtoxicity (EBE-CRO): Managing Efficacy and Toxicity in Early Oncology Clinical Trials. Journal of Biopharmaceutical Statistics (link)
  5. Delattre, M., Genon-Catalot, V. and Samson, A. (2016) Mixtures of stochastic differential equations with random effects: Application to data clustering. Journal of Statistical Planning and Inference 173 p. 109-124 (link)
  6. Colin, P., Micallef, S., Delattre, M., Mancini, P. and Parent, E. (2015) Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology. Statistics in Medicine 34(22) p. 2999-3016 (link)
  7. Delattre, M., Genon-Catalot, V. and Samson, A. (2015) Estimation of population parameters in stochastic differential equations with random effects in the diffusion coefficient. ESAIM: Probability and Statistics 19 p. 671-688 (link)
  8. Lévy-Leduc, C., Delattre, M., Mary-Huard, T. and Robin, S. (2014) Two-dimensional segmentation for analyzing HiC data. Bioinformatics 30(17) p. 386-392 (link)
  9. Delattre, M., Lavielle, M. and Poursat, M.A. (2014) A note on BIC in mixed effects models. Electronic Journal of Statistics 8(1) p. 456-475 (link)
  10. Faure, M.C., Sulpice, J.C., Delattre, M., Lavielle, M., Prigent, M., Cuif, M.H., Melchior, C., Tschirhart, E., Nusse, O. and Dupré-Crochet, S. (2013) The recruitment of p47phox and Rac2G12V at the phagosome is transient and phosphatidylserine dependent. Biology of the Cell 105 p. 1-18
  11. Delattre, M. and Lavielle, M. (2013) Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models. Statistics and Its Interface 6(4) p. 519-532 (link)
  12. Delattre, M., Genon-Catalot, V. and Samson, A. (2013) Maximum Likelihood Estimation for Stochastic Differential Equations with Random Effects. Scandinavian Journal of Statistics 40(2) p. 322-343 (link)
  13. Delattre, M., Savic, R.M., Miller, R., Karlsson, M.O. and Lavielle, M. (2012) Analysis of exposure-response of CI-945 in patients with epilepsy: application of novel mixed hidden Markov modelling methodology. Journal of Pharmacokinetics and Pharmacodynamics 39(3) p. 263-271 (link)
  14. Delattre, M. and Lavielle, M. (2012) Maximum Likelihood Estimation in Discrete Mixed Hidden Markov Models using the SAEM algorithm. Computational Statistics & Data Analysis 56(6) p. 2073-2085 (link)
  15. Delattre, M. (2010) Inference in Mixed Hidden Markov Models and Applications to Medical Studies. Journal de la Société Française de Statistique 151(1) p. 90-105 (link)


  • Delattre, M. and Kuhn, E. (2017) Estimating Fisher Information Matrix in Latent Variable Models. Preprint INRA.
  • Delattre, M. and Poursat, M.A. (2017) BIC strategies for model choice in a population approach. (arXiv:1612.02405)

Technical Reports

  • Delattre, M., Lavielle, M. and Poursat, M.A. (2012) BIC selection procedures in mixed effects models. RR-7948, INRIA.

PhD Thesis

"Inférence statistique dans les modèles mixtes à dynamique Markovienne", defended the 4th of July 2012, under the supervision of Marc Lavielle, Paris-Sud Orsay (link to manuscrit)


R package MsdeParEst dedicated to parameter estimation in stochastic differential equations with mixed effects


Some course materials