Nombre de documents

26

Liste de publications


Article dans une revue14 documents

  • Vincent Brault, Maud Delattre, Émilie Lebarbier, 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〉
  • 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, pp.1-19. 〈10.1080/10543406.2017.1295248〉. 〈hal-01533853〉
  • 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 Medecine, 2015, 〈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〉
  • 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. 〈hal-01555264v2〉
  • 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〉
  • 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, 2013, 6 (4), pp.519--532. 〈hal-00916803〉
  • 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, 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〉
  • M. Delattre, Savic R.M, R. Miller, M.O. Karlsson, M. Lavielle. Analysis of exposure-response og 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-01568772〉
  • 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. 〈http://dx.doi.org/10.1016/j.csda.2011.12.017〉. 〈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〉

Communication dans un congrès6 documents

  • Marion Tharrey, François Mariotti, Andrew Mashchak, Pierre Barbillon, Maude Delattre, et al.. Les profils de consommation protéique animale et végétale sont fortement associés à la mortalité cardiovasculaire : une analyse des données d’une large cohorte adventiste. Journées francophones de Nutrition, Nov 2016, Montpellier, France. Résumés des présentations aux JFN 2016 / Nutrition clinique et métabolisme 31 (2017), 31, pp.229 - 230, 〈10.1016/j.nupar.2017.06.033〉. 〈hal-01584563〉
  • Celine Levy Leduc, Maud Delattre, Tristan Mary-Huard, Stephane Robin. Two-dimensional segmentation for analyzing HiC data. ECCB 2014: The 13th European Conference on Computational Biology, Sep 2014, Strasbourg, France. Bioinformatics, 30 (17), pp.386-392, 2014, 〈10.1093/bioinformatics/btu443〉. 〈hal-01148580〉
  • Maud Delattre, Marc Lavielle. Equations diférentielles stochastiques en pharmacocinétique de population : modèles et méthodologie. 45e Journées de Statistique de la SfDS, May 2013, Toulouse, France. 〈http://jds2013.sfds.asso.fr/〉. 〈hal-01589553〉
  • Maud Delattre, Marc Lavielle. Population pharmacokinetics and stochastic differential equations : Models and methods. Dynstoch 2013, Apr 2013, Copenhagen, Denmark. 〈hal-01586993〉
  • Marie-Anne Poursat, Maud Delattre. Sélection de variables dans les modèles non-linéaires mixtes. 44e Journées de Statistiques - JdS'2012, May 2012, Bruxelles, Belgique. 2012, 〈http://jds2012.ulb.ac.be/〉. 〈hal-01631001〉
  • Maud Delattre. Les modèles de Markov cachés à effets mixtes. 42èmes Journées de Statistique, 2010, Marseille, France, France. 2010. 〈inria-00494812〉

Pré-publication, Document de travail4 documents

  • Maud Delattre, Marie-Anne Poursat. BIC strategies for model choice in a population approach. Prepublication arXiv:1612.02405. 2017. 〈hal-01567206〉
  • Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Estimation of the joint distribution of random effects for a discretely observed diffusion with random effects. MAP5 2017-01. 2017. 〈hal-01446063〉
  • Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Parametric inference for discrete observations of diffusion processes with mixed effects. MAP5 2016-15. 2016. 〈hal-01332630〉
  • V. Brault, M. Delattre, E. Lebarbier, T. Mary-Huard, C. Lévy-Leduc. Estimating the number of change-points in a two-dimensional segmentation model without penalization. 30 pages, 8 figures. 2015. 〈hal-01589417〉

Rapport1 document

  • Maud Delattre, Marc Lavielle, Marie-Anne Poursat. BIC selection procedures in mixed effects models. [Research Report] RR-7948, INRIA. 2012. 〈hal-00696435〉

Thèse1 document

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