Enseignant-chercheur en statistique
DOMAINES DE RECHERCHE
- Biostatistics
- Clustering and classsification
- Empirical likelihood
- Mixture models
Editorial Activity
Computational Statistics & Data Analysis (Associate Editor, 04/2021 - )
Bureau
259
Téléphone
+33 (0)2 99 05 32 15
Email
matthieu.marbac-lourdelle@ensai.fr
Adresse
ENSAI
Campus de Ker Lann
51 Rue Blaise Pascal
BP 37203
35172 BRUZ Cedex
Campus de Ker Lann
51 Rue Blaise Pascal
BP 37203
35172 BRUZ Cedex
Presentation
Since September 2017, I hold a position of Assistant Professor in Statistics at ENSAI/CREST (Bruz, France). Since September 2022, I'm also an external collaborator of the team PreMedical which is a joint team between Inria and Inserm
I defended my habilitation (french qualification required to supervise Ph.D. students) in 2022, here is my manuscript. I was awarded my Ph.D., supervised by Christophe Biernacki and Vincent Vandewalle, in October 2014 at the University of Lille 1.
For more information about, please see my academic CV.
PhD supervision
- December 2020-present: Axel Potier Probabilistic substitution model to determine the optimal stocks. Jointly supervised with Christophe Biernacki (Inria) and Vincent Vandewalle (Univ. of Nice Côte d'Azur)
- December 2021-present: Camille Méneur Arrhythmia: Statistical modelling for detection and classification of heart defects in racehorses. Jointly supervised with Gilles Stupfler (Univ. of Angers)
- September 2022-present: Antoine Bouvet Analyzing inertial swimming data for automatically monitoring athlete's activity during training. Jointly supervised with Salima El Kolei (CREST-Ensai) and Nicolas Bideau (M2S)
Collaborations
- CREST: Marie Du Roy de Chaumaray, Salima El Kolei and Valentin Patilea.
- National: Inria (Christophe Biernacki, Gilles Celeux, Julie Josse), Inserm (Orianne Dumas, Patricia Dargent, Cécilia Saldanha Gomes), Institut Paster (Étienne Patin), Université Côte d'Azur (Aude Sportisse, Vincent Vandewalle), Université de Lille (Mohamed-Salem Ahmed, Camille Frévent, Michaël Genin), Université Paris-Sud (Mohammed Sedki) and Sorbonne université (Claire Boyer).
- International: HEC Montreal (Amay Cheam, Marc Fredette) and University of McMaster (Paul McNicholas).
Preprints
- Du Roy de Chaumaray, M., El Kolei, S. and Marbac, M. Estimation of the Order of Non-Parametric Hidden Markov Models using the Singular Values of an Integral Operator.
- Sportisse, A., Marbac, M., Biernacki, C., Boyer, C., Celeux, G., Laporte, F. and Josse, J. Model-based Clustering with Missing Not At Random Data.
- Du Roy de Chaumaray, M. and Marbac, M. Full Model Estimation for Non-Parametric Multivariate Finite Mixture Models.
Book chapters (in french)
- Marbac, M. Introduction à une ́etude statistique avec données manquantes, JES 2022, sous la direction de F. Bertrand, G. Saporta, C. Thomas-Agnan.
- Marbac, M. Méthodes basées sur la vraisemblance pour données manquantes ayant un mecanisme ignorable, JES 2022, sous la direction de F. Bertrand, G. Saporta, C. Thomas-Agnan.
- Marbac, M. Méthodes de pondération pour données manquante, JES 2022, sous la direction de F. Bertrand, G. Saporta, C. Thomas-Agnan.
Journal articles (statistics)
(Most recent first)
- Cheam, A., Fredette, M., Marbac, M. and Navarro, F. Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors. Journal of the Royal Statistical Society: Series C (accepted)
- Du Roy de Chaumaray M. and Marbac M. Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models. Advances in Data Analysis and Classification (accepted) [R package MNARclust].
- Marbac, M., Sedki, M., Biernacki, C. and Vandewalle, V. (2022) Simultaneous semi-parametric estimation of clustering and regression. Journal of Computational and Graphical Statistics 31:2, 477-485,[R package ClusPred].
- Frévent, C., Ahmed, M.S., Marbac, M. and Genin, M. (2021) Detecting spatial clusters on functional data: a parametric scan statistic approach. Spatial Statistics, 46.
- Du Roy de Chaumaray, M., Marbac M. and Patilea, V. (2021) Wilks' theorem for semiparametric regressions with weakly dependent data. The Annals of Statistics, 49(6), 3228-3254.
- Biernacki, C., Marbac, M. and Vandewalle, V. (2021) Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering. Journal of Classification, 38, 129–157 [R package ClusVis].
- Du Roy de Chaumaray, M., Marbac M. and Navarro, F. (2020). Mixture of hidden Markov models for accelerometre data. The Annals of Applied Statistics, 14 (4), 1834-1855 [R package MHMM].
- Marbac, M. and Patin, E. and Sedki, M. (2020). Variable selection for mixed data clustering: Application in human population genomics. Journal of Classification, 37 124–142 [R package VarSelLCM - Package tutorial].
- Marbac, M. and Sedki, M. (2019). VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values. Bioinformatics, 35 (7), 1255-1257 [R package VarSelLCM - Package tutorial]
- Marbac, M., and Vandewalle, V. (2019). A tractable Multi-Partitions Clustering. Computational Statistics and Data Analysis, 132, 167-179.
- Marbac, M. and Sedki, M. (2017). A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data. Computational Statistics and Data Analysis, 114, 130-145 [R package MvBinary - Package tutorial].
- Cheam, A.S.M., Marbac, M. and McNicholas, P.D. (2017). Model-based clustering for spatio-temporal data on air quality monitoring. Environmetrics, 8 (3) [R package SpaTimeClus].
- Marbac, M., Biernacki, C. and Vandewalle, V. (2017). Model-based clustering of Gaussian Copulas for Mixed Data. Communications in Statistics – Theory and Methods, 46 (23) [R codes].
- Marbac, M. and Sedki, M. (2017). Variable selection for model-based clustering using the integrated complete-data likelihood. Statistics and Computing, 27 (4) [R package VarSelLCM - Package tutorial].
- Marbac, M., Biernacki, C. and Vandewalle, V. (2016). Finite mixture model of conditional dependencies modes to cluster categorical data. Advances in Data Analysis and Classification, 10 (2), 183-207 [R codes].
- Marbac, M. and McNicholas, P.D. (2016). Dimension reduction for clustering. Wiley StatsRef : Statistics Reference Online, 1–7.
- Marbac, M., Tubert-Bitter, P. and Sedki, M. (2016). Bayesian model selection in logistic regression for the detection of adverse drug reactions. Biomertical Journal, 58, 1376–1389 [R package MHTrajectoryR].
- Marbac, M., Biernacki, C. and Vandewalle, V. (2015). Model-based clustering for conditionally correlated categorical data. Journal of Classification, 32 (2), 145-175, [R codes].
Journal articles (epidemiology)
- Dumas, O., Bédard, A., Marbac, M., Sedki, M., Temam, S., Chanoine, S., Severi, G., Boutron-Ruault, M.C., Garcia-Aymerich, J., Siroux, V., Varraso, R., and Le Moual, N. (2021). Household cleaning and poor asthma control among elderly women. Journal of Allergy and Clinical Immunology: In Practice (forthcoming).
- Saldanha Gomes, C., Marbac, M., Sedki, M., Cornet, M., Plancoulainr, S., Charles, M.-A., Lioret, S. and Dargent-Molina, P. (2020). Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort. International Journal of Behavioral Nutrition and Physical Activity, 17 (20).
- Marbac, M., Sedki, M., Boutron-Ruault, M.C., and Dumas, O. (2018). Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables. The Annals of Epidemiology, 28 (8), 563-569.e6.