Maud Delattre

I am a research scientist in statistics at INRAE. I work on computational and theoretical statistics in Markovian and latent variable models. My research is motivated by applications in life sciences (pharmacology, epidemiology, plant sciences).

Contact

Mail: maud.delattre [AT] inrae.fr

Address:

INRAE UNITÉ MaIAGE

Domaine de Vilvert

78352 Jouy-en-Josas Cedex - FRANCE

Research

I am currently a member of the ANR Project Stat4Plant (Link).

Preprints

  • Delattre, M., Toda Y., Tressou, J. and Iwata, H. Modeling soybean growth: a mixed model approach. (Link)

Publications

  • Naveau, M., Kon Kam King, G., Rincent, R., Sansonnet, L. and Delattre, M. (2024) Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm. Statistics and Computing 34, 53.
  • Delattre, M., and Kuhn, E. (2023), Computing an Empirical Fisher Information Matrix Estimate in Latent Variable Models Through Stochastic Approximation. Computo (Link).
  • Baey, C., Delattre, M., Kuhn, E., Leger, J.B. and Lemler, S. (2023), Efficient preconditioned stochastic gradient descent for estimation in latent variable models. International Conference of Machine Learning (ICML).
  • Narci, R., Delattre, M., Larédo, C. and Vergu, E. (2022) Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics. Journal of Mathematical Biology 85(40)
  • Narci, R., Delattre, M., Larédo C. and Vergu, E. (2021) Inference for partially observed epidemic dynamics guided by Kalman filtering techniques. Computational Statistics & Data Analysis 164, 107319
  • Delattre, M. (2021) A review on asymptotic inference in stochastic differential equations with mixed-effects. Japanese Journal of Statistics and Data Science 4(1) p. 543-575
  • Delattre, M. and Poursat, M.A. (2020) An iterative algorithm for joint covariate and random effect selection in mixed effects models. The International Journal of Biostatistics 16(2), 20190082
  • Tharrey, M., Mariotti, F., Mashchak, A., Barbillon, P., Delattre, M. and Fraser, G. E. (2020) Patterns of amino acids intake are strongly associated with cardiovascular mortality, independently of the sources of protein. International Journal of epidemiology 49(1) p. 312-321
  • Goulnik, J., Plantureux, S., Théry, M., Baude, M., Delattre, M., Van Reeth, C., Villerd, J. and Michelot-Antalik, A. (2020) Floral trait functional diversity is related to soil characteristics and positively influences pollination function in semi-natural grasslands. Agriculture, Ecosystems & Environment 301, 10733
  • Taghipoor, M., Delattre, M. and Giger-Reverdin, S. (2020) A novel modelling approach to quantify the response of dairy goats to a high-concentrate diet. Scientific Reports 10, 20376
  • 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
  • Tharrey, M., Mariotti, F., Mashchak, A., Barbillon, P., Delattre, M. and Fraser, G. E. (2018) Patterns of plant and animal protein intake are strongly associated with cardiovascular mortality : the Adventist Health Study-2 cohort. International journal of epidemiology 47(5) p. 1603-1612
  • 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
  • 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
  • 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 17(6) p. 1-19
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Thesis and “Habilitation” (HDR)

  • HDR: Etudes de modèles markoviens à variables latentes avec applications à la pharmacologie et à l’épidémiologie. (2020)
  • Thesis: Inférence statistique dans les modèles mixtes à dynamique Markovienne. (2012)

Softwares

  • MsdeParEst (R package dedicated to parametric estimation in stochastic differential equations with mixed effects) (Link)

Teaching

2023 : Introduction to statistics and Data Analysis - MEC519S (Biomedical Engineering, Master)