Liste de publications

Maud Delattre. A review on asymptotic inference in stochastic differential equations with mixed-effects. Japanese Journal of Statistics and Data Science , 2021. ⟨hal-02938697⟩

Romain Narci, Maud Delattre, Catherine Larédo, Elisabeta Vergu. Inference for partially observed epidemic dynamics guided by Kalman filtering techniques. Computational Statistics and Data Analysis, Elsevier, 2021, 164, ⟨10.1016/j.csda.2021.107319⟩. ⟨hal-02475936v4⟩

Masoomeh Taghipoor, Maud Delattre, Sylvie Giger-Reverdin. A novel modelling approach to quantify the response of dairy goats to a high-concentrate diet. Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.20376. ⟨10.1038/s41598-020-77353-y⟩. ⟨hal-03132771⟩

Maud Delattre, Marie-Anne Poursat. An iterative algorithm for joint covariate and random effect selection in mixed effects models. International Journal of Biostatistics, De Gruyter, 2020, 16 (2), ⟨10.1515/ijb-2019-0082⟩. ⟨hal-03144783⟩

Marion Tharrey, François Mariotti, Andrew Mashchak, Pierre Barbillon, Maud Delattre, et al.. Patterns of amino acids intake are strongly associated with cardiovascular mortality, independently of the sources of protein. International Journal of Epidemiology, Oxford University Press (OUP), 2020, 49 (1), pp.312 - 321. ⟨10.1093/ije/dyz194⟩. ⟨hal-02366516⟩

Jérémie Goulnik, Sylvain Plantureux, Marc Thery, Mathilde Baude, Maud Delattre, et al.. Floral trait functional diversity is related to soil characteristics and positively influences pollination function in semi-natural grasslands. Agriculture, Ecosystems and Environment, Elsevier Masson, 2020, 301, pp.107033. ⟨10.1016/j.agee.2020.107033⟩. ⟨hal-02915375⟩

Marion Tharrey, François Mariotti, Andrew Mashchak, Pierre Barbillon, Maud Delattre, et al.. Patterns of plant and animal protein intake are strongly associated with cardiovascular mortality: the Adventist Health Study-2 cohort. International Journal of Epidemiology, Oxford University Press (OUP), 2018, 47 (5), pp.1603-1612. ⟨10.1093/ije/dyy030⟩. ⟨hal-01766695⟩

Maud Delattre, Valentine Genon-Catalot, Catherine Laredo. Parametric inference for discrete observations of diffusion processes with mixed effects. Stochastic Processes and their Applications, Elsevier, 2018, 128 (6), pp.1929-1957. ⟨10.1016/j.spa.2017.08.016⟩. ⟨hal-02622519⟩

Maud Delattre, Valentine Genon-Catalot, Catherine Laredo. Approximate maximum likelihood estimation for stochastic differential equations with random effects in the drift and the diffusion. Metrika, Springer Verlag, 2018, 81 (4), pp.953-983. ⟨10.1007/s00184-018-0666-z⟩. ⟨hal-02621496⟩

Pierre Colin, Maud Delattre, P. Minini, S. Micallef. An escalation for bivariate binary endpoints controlling the risk of overtoxicity (EBE-CRO ): managing efficacy and toxicity in early oncologyvClinical trials. Journal of Biopharmaceutical Statistics, Taylor & Francis, 2017, 27 (6), pp.1-19. ⟨10.1080/10543406.2017.1295248⟩. ⟨hal-01533853⟩

Vincent Brault, Maud Delattre, Tristan Mary-Huard, Céline Leduc. Estimating the number of block boundaries from diagonal blockwise matrices without penalization. Scandinavian Journal of Statistics, Wiley, 2017, 44 (2), pp.563-580. ⟨10.1111/sjos.12266⟩. ⟨hal-01533843⟩

Maud Delattre, Valentine Genon-Catalot, Adeline Samson. Mixtures of stochastic differential equations with random effects: Application to data clustering. Journal of Statistical Planning and Inference, Elsevier, 2016, 173, pp.109-124. ⟨10.1016/j.jspi.2015.12.003⟩. ⟨hal-01218612⟩

Maud Delattre, Valentine Genon-Catalot, Adeline Samson. Estimation of population parameters in stochastic differential equations with random effects in the diffusion coefficient. ESAIM: Probability and Statistics, EDP Sciences, 2015, 19, pp.671-688. ⟨10.1051/ps/2015006⟩. ⟨hal-01056917v2⟩

Pierre Colin, Sandrine Micallef, Maud Delattre, Pierre Mancini, Éric Parent. 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, Wiley-Blackwell, 2015, Special Issue: Papers from the Seventh International French Society of Statistics Meeting on Statistical Methods in Biopharmacy: Emerging Topics for Statistical Methodology in Clinical Drug Development, 34 (2), pp.2999-3016. ⟨10.1002/sim.6548⟩. ⟨hal-01197648⟩

Maud Delattre, Marc Lavielle, Marie-Anne Poursat. A note on BIC in mixed-effects models. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8, pp.456--475. ⟨10.1214/14-EJS890⟩. ⟨hal-00991708⟩

Maud Delattre, Marc Lavielle. Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models. Statistics and Its Interface, International Press, 2013, 6 (4), pp.519--532. ⟨hal-00916803⟩

Maud Delattre, Valentine Genon-Catalot, Adeline Samson. Maximum likelihood estimation for stochastic differential equations with random effects. Scandinavian Journal of Statistics, Wiley, 2013, 40 (2), pp.322-343. ⟨10.1111/j.1467-9469.2012.00813.x/abstract⟩. ⟨hal-00650844⟩

Marie-Cécile Faure, Jean-Claude Sulpice, Maud Delattre, Marc Lavielle, Magali Prigent, et al.. The recruitment of p47phox and Rac2G12V at the phagosome is transient and phosphatidylserine-dependent. Biology of the Cell, Wiley, 2013, 105, pp.1--18. ⟨10.1111/boc.201300010⟩. ⟨hal-01555264v2⟩

Maud Delattre, Radojka M. Savic, Raymond Miller, Mats O. Karlsson, Marc Lavielle. Analysis of exposure–response of CI-945 in patients with epilepsy: application of novel mixed hidden Markov modeling methodology. Journal of Pharmacokinetics and Pharmacodynamics, Springer Verlag, 2012, 39 (3), pp.263 - 271. ⟨10.1007/s10928-012-9248-2⟩. ⟨hal-01560511⟩

Maud Delattre, Radojka Savic, Raymond Miller, Mats Karlsson, Marc Lavielle. Analysis of exposure-response of CI-945 in patients with epilepsy: application of novel Mixed Hidden Markov Modelling Methodology. Journal of Pharmacokinetics and Pharmacodynamics, Springer Verlag, 2012, 39 (3), pp.263-271. ⟨hal-00756603⟩

Maud Delattre, Marc Lavielle. Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm. Computational Statistics and Data Analysis, Elsevier, 2012, 56 (6), pp.2073-2085. ⟨10.1016/j.csda.2011.12.017⟩. ⟨hal-00756599⟩

Maud Delattre. Inference in Mixed Hidden Markov Models and Applications to Medical Studies. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2010, 151 (1), pp.90-105. ⟨hal-00637419⟩

Romain Narci, Maud Delattre, Catherine Larédo, Elisabeta Vergu. Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics. 2021. ⟨hal-03347365⟩

Maud Delattre, Estelle Kuhn. Estimating Fisher Information Matrix in Latent Variable Models based on the Score Function. 2019. ⟨hal-02285712⟩

Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Estimation of the joint distribution of random effects for a discretely observed diffusion with random effects. 2016. ⟨hal-01446063⟩

Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Parametric inference for discrete observations of diffusion processes with mixed effects. 2016. ⟨hal-01332630⟩

Estelle Kuhn, Maud Delattre. Estimating Fisher Information Matrix in Latent Variable Models. [Technical Report] 2017. ⟨hal-02790629⟩

Maud Delattre, Marc Lavielle, Marie-Anne Poursat. BIC selection procedures in mixed effects models. [Technical Report] RR-7948, INRIA. 2012. ⟨hal-00696435⟩

Maud Delattre. Inférence statistique dans les modèles mixtes à dynamique Markovienne. Applications [stat.AP]. Université Paris Sud - Paris XI, 2012. Français. ⟨tel-00765708⟩

Maud Delattre. Etudes de modèles markoviens à variables latentes avec applications à la pharmacologie et à l’épidémiologie.. Statistiques [math.ST]. Université Paris-Saclay, 2020. ⟨tel-03202055⟩