Professeure Première classe à l'Université d'Evry.
Depuis septembre 2023, je suis co-responsable avec Christophe Ambroise du Master 2, Data Science: santé, Assurance, Finance. (http://www.math-evry.cnrs.fr/departement/doku.php?id=formation:master:m2ds, Evry)
Master 2 Data Science Santé, Assurance, Finance à University Paris Saclay.

M1 MINT et the Master of Mathematics and Applications de l'Université Paris Saclay.

• Modèles semi-paramétriques
• Estimation non-paramétriqu
• Analyse de données de survie
• Réduction de dimension et sélection de variables
• Sélection de modèles
• Modèles de mélange
• Déconvolution
• Modèles avec erreurs
• Analyse de sensibilité
• Construction de méta-modèles

• Odelia Guedj started this phd thesis in september 2020 under the supervision of Agathe Guilloux . She is working on sensitivity analysis in linear and survival models. She is receiving a 3-year financial support from the the EDMH.

• Halala Kamari started his thesis in september, 2016, under the supervision of Sylvie Huet, MaIAGE Unit in INRA, Jouy-En-Josas. She is working half time at our laboratory, spending the rest of the time in INRA. She is working on meta-modelling and sensitivity. She is receiving a 3-year financial support from the EDMH. She has presented his Phd defense on july 2020.
• Marius Kwemou started his thesis in July, 2010, under the supervision of Jean-Yves Lehesran, Abdou Kâ Diongue from University Gaston Berger at Saint-Louis in Senegal and myself. He is working half time at our laboratory, spending the rest of the time in University Gaston Berger at Saint-Louis in Senegal. He is working on dimension reduction in logistic regression with an application to ACTU-PALU Data (ANR n°07-SEST-001). He is receiving a 3-year financial support from the IRD. He has presented his Phd defense on september, the 29th, 2014.
• Sarah Lemler started her thesis in september, 2011, under the supervision of Agathe Guilloux and myself. She is receiving a 3-year financial support from the french minister of Education and Research and Evry University. She is working on variable selection and dimension reduction in high-dimensional survival analysis. She has presented her Phd defense on december, the 9th, 2014.

Groupe de travail de Statistique de Paris 5

Séminaire Parisien de Statistique

GdR MASCOT-NUM

Réseau Mexico

Réseau REM

Linear Models and R

Logistic Regression. A self learning Text

Generalized Linear Models

• P1 Clémence Frioux, Sylvie Huet, Simon Labarthe, Julien Martinelli, Thibault Malou, et al.. Accelerating metabolic models evaluation with statistical metamodels: application to Salmonella infection models. 2022. https://hal.inrae.fr/hal-03635862v2 ESAIM: PROCEEDINGS AND SURVEYS, September 2023, Vol. 73, p. 187-217

• P2 Kamari H, Huet S. et Taupin M.-L., “The Journal of Statistical Software: RKHSMetaMod: An R Package to Estimate the Hoeffding Decomposition of a Complex Model by Solving RKHS Ridge Group Sparse Optimization Problem”, The R Journal, 2022 https://arxiv.org/abs/1905.13695v6 et https://hal.science/hal-02366422v1

• P3 RKHSMetamod R package 2019 RKHSMetamod

• P4 Ferrer Savall, J., Franqueville, D., Barbillon, P., Benhamou, C., Durand, P. , Taupin,M.-L., Monod, H. et Drouet, J.-L. Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale. Environnemental Modelling & Softeware Vol 111:356-367(2019).

• P5 Huet, S. et Taupin, M.-L. Metamodel construction for sensitivity analysis. (2017). ESAIM: PROCEEDINGS AND SURVEYS, Vol. 60, p. 27-69. (2017) https://hal.archives-ouvertes.fr/hal-01434895

• P6 Ferrer Savall, J., Franqueville, D., Benhamou, C., Barbillon, P., Durand, P. , Taupin, M.-L., Monod, H. et Drouet, J.-L. Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale. Proceedings of Spatial Accuracy (2016) https://arxiv.org/abs/1709.08608v1

• P7 Guilloux, A., Lemler, S. , Taupin, M.-L. Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates. Journal of Statistical Planning and Inference, 171:38-62, Elsevier, (2016). https://arxiv.org/abs/1503.00226v2

• P8 Guilloux, A., Lemler, S., Taupin, M.-L. Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates. Journal of Multivariate Analysis, 148:141-159, Elsevier, (2016). https://arxiv.org/abs/1507.01397v1

• P9 Dedecker J., Samson A. et Taupin M.-L.: Estimation in autoregressive model with measurement error. (2014) ESAIM P&S. http://arxiv.org/abs/1105.1310

• P10 Taupin, M.-L. Comment on : Identification and estimation of nonlinear models using two samples with nonclassical measurement errors. Journal of nonparametric statistics 22 (2010) pp 409-414.

• P11 Comte F., Dedecker J. et Taupin M.-L Adaptive density deconvolution with dependent inputs. Math. Methods Statist., 17 (2008), pp87-112. http://www.springerlink.com/content/22584x673251m076/

• P12 Martin-Magniette M.-L. et Taupin M.-L. Semi-parametric estimation of the hazard function in a model with covariate measurement error. ESAIM Probab&Stat. 13 (2009), pp87-114 http://fr.arxiv.org/abs/math.ST/0606192

• P13 Comte F., Dedecker J. et Taupin M.-L Adaptive density estimation for general ARCH models. Econometric Theory 24(6) 2008, pp. 1628-1662. http://journals.cambridge.org/action/displayJournal?jid=ECT.

• P14 Butucea C. et Taupin M.-L. New M-estimators in semiparametric regression with errors-in- variables.Annales de l’Institut Henri Poincaré - Probabilités et Statistique 44 (2008) no.3, pp. 393–421. http://fr.arxiv.org/abs/math.ST/0511105

• P15 Comte F. et Taupin M.-L. Nonparametric estimation of the regression function in an errors-in-variables model. Statistica Sinica 17 (2007), 1065-1090. http://fr.arxiv.org/abs/math.ST/0511111

• P16 Comte F., Rozenholc Y. et Taupin M. L. Finite sample penalization in adaptive density deconvolution. Journal of Statistical Computation and Simulation, 77 (2007), Issue 11, pp. 977-1000. http://fr.arxiv.org/abs/math.ST/0601098

• P17 Comte F., Rozenholc Y. et Taupin M.-L. Penalized contrast estimator for adaptive density deconvolution. The Canadian Journal of Statistics 34 (2006), No 3, pp. 431-452. http://fr.arxiv.org/abs/math.ST/0601091

• P18 Matias, C. Taupin, M.-L. Minimax estimation of linear functionals in the convolution model. Math. Methods Statist. 13 (2004), no. 3, pp. 282–328.

• P19 Comte, F. Taupin, M.-L. Semiparametric estimation in the (auto)-regressive  -mixing model with errors-in-variables. Math. Methods Statist. 10 (2001), no. 2, pp. 121–160.

• P20 Taupin, M.-L. Semi-parametric estimation in the nonlinear structural errors-in-variables model. Ann. Statist. 29 (2001), no. 1,66–93

• Frioux, Clémence and Huet, Sylvie and Labarthe, Simon and Martinelli, Julien and Malou, Thibault and Sherman, David James and Taupin, Marie-Luce and Salas, Pablo Ugalde, Poster . Dynamic Flux Balance Analysis with Metamodels JOBIM 2022 (Société Française de BioInoformatique) https://hal.science/hal-04236775 ,

• Frioux, Clémence and Huet, Sylvie and Labarthe, Simon and Martinelli, Julien and Malou, Thibault and Sherman, David James and Taupin, Marie-Luce and Ugalde-Salas, Pablo, Poster . Dynamic Flux Balance Analysis with Metamodels Microbial communities: current approaches and open challenges 2022 https://hal.science/hal-04252041

• S1 Huet, S., Kamari, H. et Taupin, M.-L. Metamodel construction for sensitivity analysis in the context of dependent input and sub-gaussian noise. En cours de rédaction

• S2 Kwemou, M. et Taupin, M.-L. et Tocquet, A.-S. Model selection in Logistic regression. (2014). En révision https://hal.archives-ouvertes.fr/hal-01188376

• S3 Kwemou, M. et Taupin, M.-L. et Dos Santos, S. et Le Hesran, J.-Y. Variables Selection strategies in large socio-epidemiological surveys. Identification of households at risk of having febrile episode in Dakar, Sénégal. (2014) Unpublished.

• 2008 Habilitation à Diriger des Recherches, soutenue le 09 décembre 2008 à l’Université Paris Descartes. Déconvolution et Estimation dans les modèles avec erreurs sur les variables. Habilitation

• 1994-1998 Thèse de Doctorat de l’Université Paris-Sud. effectuée au Laboratoire de Biométrie, INRA (Jouy-en-Josas) sous la direction de Sylvie Huet (INRA) et P. Massart (Univ. Paris-Sud). Estimation semi-paramétrique pour le modèle de régression non linéaire avec erreurs sur les variables.https://www.theses.fr/1998PA112004| Thèse