Publications

William MARGERIT, Antoine CHARPENTIER, Cathy MAUGIS-RABUSSEAU, Johann Christian SCHON, Nathalie TARRAT, Juan CORTES (2023). IGLOO: An Iterative Global Exploration and Local Optimization Algorithm to Find Diverse Low-Energy Conformations of Flexible Molecules. Algorithms, 16, 476.

François BACHOC, Cathy MAUGIS-RABUSSEAU, Pierre NEUVIAL (2023). Selective inference after convex clustering with l1 penalization.

Cathy MAUGIS-RABUSSEAU (2022). Quelques contributions autour des modèles de mélanges et pour l'analyse de données transcriptomiques. HDR, Université Paul Sabatier Toulouse 3.

Elodie BERNARD, Thomas PEYRET, Mathilde PLINET, Yohan CONTIE, Thomas CAZAUDARRE, Yannick ROUQUET, Matthieu BERNIER, Stéphanie PESANT, Richard FABRE, Aurore ANTON, Cathy MAUGIS-RABUSSEAU, Jean-Marie FRANCOIS (2022). The DendrisCHIP® Technology as a New, Rapid and Reliable Molecular Method for the Diagnosis of Osteoarticular Infections. Special Issue of Diagnostic Infectious Disease and Microbiology, 12(6), 1353.

Yohann De CASTRO, Sébastien GADAT, Clément MARTEAU, Cathy MAUGIS-RABUSSEAU (2021). Supermix : sparse regularization for mixtures.. Annals of Statistics, 49(3): 1779-1809.

Sevan ARABACIYAN, Michael SAINT-ANTOINE, Cathy MAUGIS-RABUSSEAU, Jean-Marie FRANCOIS, Abhyudai SINGH, Jean-Luc PARROU, Jean-Pascal CAPP (2021). Insights on the control of yeast single-cell growth variability by members of the Trehalose Phosphate Synthase (TPS) complex. Frontiers in cell and developmental biology, (9).

Antoine GODICHON-BAGGIONI, Cathy MAUGIS-RABUSSEAU, Andréa RAU (2020). Multiview cluster aggregation and splitting, with an application to multiomic breast cancer data. The Annals of Applied Statistics, (14), 2, pp. 752 – 767.

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Sébastien GADAT, Jonas KAHN, Clément MARTEAU, Cathy MAUGIS-RABUSSEAU (2020). Parameter recovery in two-component contamination mixtures: The $L^2$ strategy. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques, 56(2), pp. 1391–1418.

Gilles CELEUX, Cathy MAUGIS-RABUSSEAU, Mohammed SEDKI (2019). Variable selection in model-based clustering and discriminant analysis with a regularization approach. Advances in Data Analysis and Classification, (13), 1, pp. 259–278.

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Antoine GODICHON-BAGGIONI, Cathy MAUGIS-RABUSSEAU, Andréa RAU (2019). Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. Journal of Applied Statistics, 46, pp. 47–65.

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Andréa RAU, Cathy MAUGIS-RABUSSEAU (2018). Transformation and model choice for RNA-seq co-expression analysis. Briefings in Bioinformatics, 19(3), pp. 425–436.

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Guillem RIGAILL, Sandrine BALZERGUE, Veronique BRUNAUD, Eddy BLONDET, Andréa RAU, Odile ROGIER, Jose CAIUS, Cathy MAUGIS-RABUSSEAU, Ludivine SOUBIGOU-TACONNAT, Sebastien AUBOURG (2018). Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis. Briefings in Bioinformatics, 19(1), pp. 65–76.

Béatrice LAURENT-BONNEAU, Clément MARTEAU, Cathy MAUGIS-RABUSSEAU (2018). Multidimensional two-component Gaussian mixtures detection. Annales de l’Institut Henri Poincar'e, Probabilit'es et Statistiques (Série B), 54(2), pp. 842–865.

Christophe BIERNACKI, Cathy MAUGIS-RABUSSEAU (2017). Chapter 9: High-dimensional clustering.. Model choice and model aggregation, under the direction of F.BERTRAND, J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition Technip, 2017.

Marie-Laure MARTIN-MAGNIETTE, Cathy MAUGIS-RABUSSEAU, Andréa RAU (2017). Chapter 10 : Clustering of co-expressed genes.. Model choice and model aggregation, under the direction of F.BERTRAND, J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition Technip, 2017.

Panagiotis PAPASTAMOULIS, Marie-Laure MARTIN-MAGNIETTE, Cathy MAUGIS-RABUSSEAU (2016). On the estimation of mixtures of Poisson regression models with large number of components. Computational Statistics & Data Analysis, (93), pp. 97–106.

Béatrice LAURENT-BONNEAU, Clément MARTEAU, Cathy MAUGIS-RABUSSEAU (2016). Non-asymptotic detection of two-component mixtures with unknown means. Bernoulli, 22(1), pp. 242–274.

Andréa RAU, Cathy MAUGIS-RABUSSEAU, Marie-Laure MARTIN-MAGNIETTE, Gilles CELEUX (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. In: Bioinformatics, 31(9), pp. 1420–1427.

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Gilles CELEUX, Marie-Laure MARTIN-MAGNIETTE, Cathy MAUGIS-RABUSSEAU, Adrian E RAFTERY (2014). Comparing model selection and regularization approaches to variable selection in model-based clustering. Journal de la Societe Francaise de Statistique, 155(2), pp. 57–71.

Cathy MAUGIS-RABUSSEAU, Bertrand MICHEL (2013). Adaptive density estimation for clustering with Gaussian mixtures. ESAIM: Probability and Statistics, 17, pp. 698–724.

Jean-Patrick BAUDRY, Cathy MAUGIS, Bertrand MICHEL (2012). Slope heuristics: overview and implementation. Statistics and Computing, 22(2), pp. 455–470.

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Cathy MAUGIS-RABUSSEAU, Marie-Laure MARTIN-MAGNIETTE, Sandra PELLETIER (2012). SelvarClustMV: Variable selection approach in model-based clustering allowing for missing values. Journal de la Société Française de Statistique, (153), 2, pp. 21–36.

Caroline MEYNET, Cathy MAUGIS-RABUSSEAU (2012). A sparse variable selection procedure in model-based clustering.

Cathy MAUGIS, Gilles CELEUX, Marie-Laure MARTIN-MAGNIETTE (2011). Variable selection in model-based discriminant analysis. Journal of Multivariate Analysis, 102(10), pp. 1374–1387.

Cathy MAUGIS, Bertrand MICHEL (2011). Data-driven penalty calibration: a case study for Gaussian mixture model selection. ESAIM: Probability and Statistics, 15, pp. 320–339.

Cathy MAUGIS, Bertrand MICHEL (2011). A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: Probability and Statistics, 15, pp. 41–68.

Cathy MAUGIS, Gilles CELEUX, Marie-Laure MARTIN-MAGNIETTE (2009). Variable selection in model-based clustering: A general variable role modeling. Computational Statistics & Data Analysis, 53 (11), pp. 3872–3882.

Cathy MAUGIS, Gilles CELEUX, Marie-Laure MARTIN-MAGNIETTE (2009). Variable selection for clustering with Gaussian mixture models. Biometrics, (65 (3), pp. 701–709.

Cathy MAUGIS, Marie-Laure MARTIN-MAGNIETTE, Jean-Philippe TAMBY, Jean-Pierre RENOU, Alain LECHARNY, Sebastien AUBOURG, Gilles CELEUX (2009). Sélection de variables pour la classification par mélanges gaussiens pour prédire la fonction des gènes orphelins. La revue MODULAD, 40, pp. 69–80.

Cathy MAUGIS (2008). Variable selection for model-based clustering. Application for transcriptome data analysis. PhD Thesis, University Paris-Sud 11.