MARBAC LOURDELLE Matthieu

Preprints

1. Cheam, A., Fredette, M., Marbac, M. and Navarro, F. Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors.
   
2. Frévent, C., Ahmed, M.S., Marbac, M. and Genin, M. Detecting spatial clusters on functional data: a parametric scan statistic approach.
   
3. Du Roy de Chaumaray M. and Marbac M. Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models.
   
4. Marbac, M., Sedki, M., Biernacki, C. and Vandewalle, V. Simultaneous semi-parametric estimation of clustering and regression.
   
   
   

Journal articles (statistics)

(Most recent first)

• Du Roy de Chaumaray, M., Marbac M. and Patilea, V. Wilks' theorem for semiparametric regressions with weakly dependent data. The Annals of Statistics, forthcoming.
• Biernacki, C., Marbac, M. and Vandewalle, V. Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering. Journal of Classification, forthcoming [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.