MARBAC LOURDELLE Matthieu

Preprints

• Du Roy de Chaumaray M. and Marbac M
  Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models
  
  
• Marbac, M., Sedki, M., Biernacki, C. and Vandewalle, V.
  Unifying regression and clustering for latent group effect models.
  
  
• Du Roy de Chaumaray, M., Marbac M. and Patilea, V.
  Wilks’ theorem for semiparametric regressions with weakly dependent data. [arXiv]
  
  

Journal articles

• Du Roy de Chaumaray, M., Marbac M. and Navarro, F.
  Mixture of hidden Markov models for accelerometer data.
  Annals of Applied Statistics, forthcoming. [Journal - arXiv]

• Biernacki, C., Marbac, M. and Vandewalle, V.
  Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering.
  Journal of Classification, forthcoming [Journal - Hal]
  
  
• 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 [Journal - R package VarSelLCM.2.1 - Package tutorial]
  
  
• 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  [Journal]
  
  
• 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 [Journal]

• Marbac, M., and Vandewalle, V. (2019)
  A tractable Multi-Partitions Clustering.
  Computational Statistics and Data Analysis, 132, 167-179 [Journal]

• 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.
  Annals of Epidemiology, 28 (8), 563-569.e6 [Journal]

• 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 [Journal - R package MvBinary.1.0 - 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) [Journal - R package SpaTimeClus.1.0]

• 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) [Journal - 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) [Journal - 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 [Journal - R codes]

• Marbac, M. and McNicholas, P.D. (2016)
  Dimension reduction for clustering.
  Wiley StatsRef : Statistics Reference Online, 1–7 [Journal]

• 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 [Journal - R package MHTrajectoryR.1.0]

• Marbac, M., Biernacki, C. and Vandewalle, V. (2015)
  Model-based clustering for conditionally correlated categorical data.
  Journal of Classification, 32 (2), 145-175,  [Journal - R codes]

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].