Matthieu MARBAC

Matthieu MARBAC

Enseignant-chercheur en statistique

Ensai - Campus de Ker Lann - Rue Blaise Pascal - BP 37203
35172 Bruz Cedex
FRANCE

Tél. : +33 (0)2 99 05 32 73
  • Publications

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, (forthcoming) [Journal]
  • Marbac, M., and Vandewalle, V.
    A tractable Multi-Partitions Clustering.
    Computational Statistics and Data Analysis
    , (forthcoming) [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 Patin, E. and Sedki, M.
    Variable selection for mixed data clustering: Application in human population genomics.
    Journal of Classification
    , (forthcoming) [R package VarSelLCM.2.1 - Package tutorial]
  • 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.

Works in progress

  • with Patilea, V.
    Empirical likelihood for conditional estimating equations.
  • with Biernacki, C., Sedki, M. and Vandewalle, V.
    Multiple model-based clustering to improve model-based prediction.

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].