Recherche

MARBAC LOURDELLE Matthieu

Enseignant-chercheur en statistique DOMAINES DE RECHERCHE
  • Model-based clustering
  • Computational statistics
  • Biostatistics
  • Empirical likelihood
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

Journal articles

  • Marbac, M. and Sedki, M.
    VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values.
    Bioinformatics, 35 (7), 1255-1257, 2019
    [Journal]
  • Marbac, M., and Vandewalle, V.
    A tractable Multi-Partitions Clustering.
    Computational Statistics and Data Analysis
    , 132, 167-179, 2019 [Journal]
  • Marbac, M., Sedki, M., Boutron-Ruault, M.C., and Dumas, O.
    Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables.
    Annals of Epidemiology
    , 28 (8), 563-569.e6, 2018 [Journal]
  • Marbac, M. and Sedki, M.
    A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data.
    Computational Statistics and Data Analysis
    , 114, 130-145, 2017 [Journal - R package MvBinary.1.0 - Package tutorial]
  • Cheam, A.S.M., Marbac, M. and McNicholas, P.D.
    Model-based clustering for spatio-temporal data on air quality monitoring.
    Environmetrics
    , 8 (3), 2017 [Journal - R package SpaTimeClus.1.0]
  • Marbac, M., Biernacki, C. and Vandewalle, V.
    Model-based clustering of Gaussian Copulas for Mixed Data.
    Communications in Statistics – Theory and Methods
    , 46 (23), 2017 [Journal - R codes]
  • Marbac, M. and Sedki, M.
    Variable selection for model-based clustering using the integrated complete-data likelihood.
    Statistics and Computing
    , 27 (4), 2017. [Journal - R package VarSelLCM - Package tutorial]
  • Marbac, M., Biernacki, C. and Vandewalle, V.
    Finite mixture model of conditional dependencies modes to cluster categorical data.
    Advances in Data Analysis and Classification
    , 10 (2), 183-207, 2016. [Journal - R codes]
  • Marbac, M. and McNicholas, P.D.
    Dimension reduction for clustering.
    Wiley StatsRef : Statistics Reference Online
    , 1–7, 2016. [Journal]
  • Marbac, M., Tubert-Bitter, P. and Sedki, M.
    Bayesian model selection in logistic regression for the detection of adverse drug reactions.
    Biomertical Journal
    , 58, 1376–1389, 2016. [Journal - R package MHTrajectoryR.1.0]
  • Marbac, M., Biernacki, C. and Vandewalle, V.
    Model-based clustering for conditionally correlated categorical data.
    Journal of Classification
    , 32 (2), 145-175, 2015. [Journal - R codes]

Preprints

  • Biernacki, C., Marbac, M. and Vandewalle, V.
    Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering. [Hal]

  • Du Roy de Chaumaray, M., Marbac M. and Navarro, F.
    Mixture of hidden Markov models for accelerometer data. [arXiv]

  • Saldanha Gomes, C., Marbac, M., Sedki, M.,  Cornet, M., Plancoulainr, S., Charles, M.-A., Lioret, S. and Dargent-Molina, P.
    Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort.

Works in progress

  • with Patilea, V.
    On the Wilks' Theorem for semiparametric regression model.

  • with Biernacki, C., Sedki, M. and Vandewalle, V.
    On the use of the cluster memberships in regression models.

Proceedings

  • Marbac, M., Biernacki, C. and Vandewalle, V.
    Mixture model of Gaussian copulas.
    Proceedings CompStat, 2014
    [Pdf]

Other document

  • Marbac, M.
    Model-based clustering for categorical and mixed vairables.
    Thèse de doctorat, 2014
    [Pdf].