Publications


Journal papers

  1. Neuvial, P., Randriamihamison, N., Chavent, M., Froissac, S., Vialaneix, N. (2024), A two-sample tree-based test for hierarchically organized genomic signals, Journal of the Royal Statistical Society Series C: Applied Statistics, https://doi.org/10.1093/jrsssc/
  2. Chavent, M., Kuentz, V., Labenne, A., and Saracco, J. (2022), Multivariate analysis of mixed data: The PCAmixdata R package,  Electronic Journal of Applied Statistical Analysis, Vol. 15, Issue 03, pp. 606-645.
  3. Chavent, M., Genuer, R., Saracco, J. (2021). Combining clustering of variables and feature selection using random forests, Communications in Statistics – Simulation and Computation, Vol 50, No 2, pp. 426-445.
  4. Ellies-Oury, M.-P., Chavent, M., Conanec, A., Picard, B., Saracco, J. (2019). Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness. Scientific Reports, Vol 9, No 10014.
  5. Chavent, M., Kuentz-Simonet, V., Labenne, A., Saracco, J. (2018). ClustGeo: an R package for hierarchical clustering with spatial constraints. Computational Statistics, Vol 33, No 4, pp. 1799-1822.
  6. Saracco J., Chavent M., Audin-Garcia L., Lespinet-Najib V., Ron-Angevin R. (2018). Classification de variables et analyse multivariée de données mixtes issues d’une étude BCI. Ingénierie cognitique, Vol 1, No 2.
  7. Vézard, L., Legrand, P., Chavent, M., Faita-Ainseba, F., Trujillo, L. (2015). EEG classification for the detection of mental states. Applied Soft Computing, Vol. 32, pp. 113-131.
  8. Chavent, M., Girard, S., Kuentz, V. Liquet, B. Nguyen, T.M.N. and Saracco, J. (2014).  A sliced inverse regression approach for data stream. Computational Statistics, Vol. 29, pp. 1129-1152.
  9. Kuentz-Simonet, V., Lyser, S., Candau, J., Deuffic, P., Chavent, M., Saracco, J. (2013). Une approche par classification de variables pour la typologie d’observations : le cas d’une enquête agriculture et environnement. Journal de la SFdS, Vol. 154, No 2.
  10. Chavent, M., Kuentz., V. and Saracco, J. (2012). Orthogonal Rotation in PCAMIX. Advances in Classification and Data Analysis, Vol. 6, pp. 131-146.
  11. Chavent, M., Liquet, B., Kuentz-Simonet and V., Saracco, J. (2012). ClustOfVar: An R Package for the Clustering of Variables. Journal of Statistical Software, Vol. 50, pp. 1-16.
  12. Josse, J., Chavent, M., Liquet B. and Husson, F. (2012). Handling missing values with Regularized Iterative Multiple Correspondence Analysis. Journal of classification, Vol. 29, pp. 91-116.
  13. Chavent, M., Kuentz, V., Liquet B. and Saracco J. (2011). Sliced Inverse Regression for stratified population. Communications in Statistics – Theory and methods, Vol. 40, pp. 3857-3978.
  14. Chavent, M., Liquet B. and Saracco J. (2010). A semiparametric approach for multivariate sample selection. Statistica Sinica, Vol. 20, pp. 513-536.
  15. Chavent, M., Guégan, H., Kuentz, V., Patouille, B. and Saracco J. (2009). PCA and PMF based methodology for air pollution sources identification and apportionment. Environmetrics, Vol. 20, pp. 928-942.
  16. Chavent, M. and Saracco J. (2008). Central tendency and dispersion measures for intervals and hypercubes. Communications in Statistics – Theory and methods, Vol. 37, pp. 1471-1482.
  17. Chavent, M., Guégan, H., Kuentz, V., Patouille, B. and Saracco J. (2007). Air pollution sources apportionment in a french urban site. Case Studies in Business, Industry and Government Statistics journal, Vol. 1, pp. 119-129.
  18. Chavent, M., Briant O. and Lechevallier, Y. (2007). DIVCLUS-T: a monothetic divisive hierarchical clustering method, Computational Statistics and Data Analysis, Vol. 32, pp. 687-701.
  19. Chavent, M., Kuentz, V. and Saracco, J. (2007).  Analyse en Facteurs : présentation et comparaison des logiciels SAS, SPAD et SPSS. La Revue Modulad, Vol. 37, pp. 1-30.
  20. Chavent, M., De Carvhalo, F. de A.T., Lechevallier, Y. and Verde, R. (2006). New Clustering methods for interval data. Computational Statistics, Vol. 21, pp. 211-229.
  21. Carvhalo, F. de A.T., Souza, R. M.C.R., Chavent, M. and Lechevallier, Y. (2006). Adaptative Hausdorff Distances and Dynamic Clustering of Symbolic Interval Data. Pattern Recognition Letters, Vol. 27, pp. 167-179.
  22. Chavent, M., Carvhalo, F. de A.T., Lechevallier, Y. and Verde, R. (2003). Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle. Revue de Statistique Appliquée, Vol. 51, pp. 5-29.
  23. Chavent, M. and  Patouille, B. (2003). Calcul des coefficients de régression et du PRESS en régression PLS1. La Revue de Modulad, Vol. 30, pp. 1-9.
  24. Chavent, M., Lacomblez, C., Boudou, A. and Maury-Brachet, R. (2001). Contamination par le mercure et classification d’espèces en écotoxicologie : approche classique, approche symbolique. La Revue Modulad, Vol. 26, pp. 19-32.
  25. Chavent, M., Guinot, C., Lechevallier, Y. and Tenenhaus, M. (1999). Méthodes divisives de classification et segmentation non supervisée : recherche d’une typologie de la peau humaine saine. Revue de Statistique Appliquée, Vol. 47, pp. 87-99.
  26. Chavent, M. (1998). A monothetic clustering method. Pattern Recognition Letters, Vol. 19, pp. 989-996.
  27. Chavent, M. and Touati, M. (1997). Recodage et classification symbolique d’un tableau de données temporelles. Application à l’étude du comportement d’utilisateurs. Revue de Statistique Appliquée, Vol 45, pp. 73-88.


Preprints

  1. Chavent, M. Chavent, G., From explained variance of correlated components to PCA without orthogonality constraints. Working paper or preprint (2024). https://inria.hal.science/hal-04442489.


Conference papers

  1. Chavent, M., Darmendrail, V., Lorenzo, H., Pourtier, F., Saracco, J. (2023), A new statistical methodology to detect earnings management, 37th International Workshop on Statistical Modelling (IWSM 2023), Dortmund, pp. 394-200.
  2. Chavent, M., Cottrell, M., Lacaille, J., Mourer, A., Olteanu, M. (2022). Sparse Weighted K-Means for Groups of Mixed-Type Variables. In: Faigl, J., Olteanu, M., Drchal, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, https://doi.org/10.1007/978-3-031-15444-7_1
  3. Chavent M., Lacaille J., Mourer A., Olteanu M. (2021), Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability?, in ESANN 2021, i6doc.com, 569-574, https://doi.org/10.14428/esann/2021.ES2021-155
  4. Chavent, M., Lacaille, J., Mourer, A., Olteanou, M. (2020). Sparse k-means for mixed data via group-sparse clustering. In ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning, i6doc.com publ., ISBN 978-2-87587-074-2.
  5. Mourer, A., Lacaille, J., Olteanu, M., Chavent, M. (2020). Automatic detection of rare observations during production tests using statistical models. Annual Conference of the PHM Society, Vol 12, No 1.
  6. Vezard, L., Legrand, P., Chavent, M., Faita-Ainseba, F., Clauzel, J., Trujillo, L. (2014). Classification of EEG signals by evolutionary algorithm. In Advances in Knowledge Discovery and Management Volume 4, F. Guillet , B. Pinaud , G. Venturini , D. Zighed (editors), Studies in Computational Intelligence, vol. 527, pp. 133-153, Springer.
  7. Todeschini, A., Caron, F., Chavent, M. (2013). Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms. In Advances in Neural Information Processing Systems (NIPS 2013), pp. 845-853.
  8. Vézard, L., Legrand, P., Chavent, M., Faita-Ainseba, F., Trujillo, L. (2013).  Detecting mental states of alertness with genetic algorithm variable selection. In Proceedings of IEEE Congress on Evolutionary Computation (CEC 2013), pp. 1247-1254.
  9. Brito, P. and Chavent, M. (2012).  Divisive Monothetic Clustering for Interval and Histogram-valued Data. In Proceedings of the ICPRAM’2012, Vol. 1, pp. 229-234, SciTePress.
  10. Vézard, L., Legrand, P., Chavent, M., Faita-Ainseba, F. and Clauzel, J. (2012). Classification de données EEG par algorithme évolutionnaire pour l’étude d’états de vigilance. Revue des Nouvelles Technologies de l’Information (Proceedings EGC’12), Editions Herman.
  11. Chavent, M, Kuentz, V. and Saracco, J. (2009). A Partitioning Method for the clustering of categorical variables. In Classification as a Tool for Research (Proceedings of the 11th IFCS’2009), pp. 90-99, Springer.
  12. Chavent, M., Lechevallier, Y., Vernier, F. and Petit, K. (2008). Monothetic divisive clustering with geographical constraints. In Proceedings in Computational Statistics 2008, pp. 67-76, Physica-Verlag.
  13. Chavent, M., Lechevallier, Y. (2006). Empirical comparison of a monothetic divisive clustering method with the Ward and the k-means clustering methods. In Data Science and Classification (Proceedings of IFCS 2006), pp. 83-90, Springer.
  14. Chavent, M. (2005). Normalized k-means clustering of hyper-rectangles. In Proceedings of the XIth International Symposium of Applied Stochastic Models and Data Analysis (ASMDA 2005), pp. 670-677.
  15. Chavent, M. (2004).  An Hausdorff distance between hyper-rectangles for clustering interval data. In Classification, Clustering and Data Mining Applications (Proceedings of IFCS 2004), pp. 333-340, Springer.
  16. Chavent, M. and Lechevallier, Y. (2002).  Dynamical Clustering of interval data. Optimization of an adequacy criterion based on Hausdorff distance. In Classification, Clustering and Data Analysis (Proceedings of IFCS 2002), pp. 53-60, Springer.
  17. Chavent, M. and Stephan, V. (1998).  From Generalization to clustering in the Relational Database Context. Proceedings of the conference on Knowledge Extraction and Symbolic Data Analysis (KESDA’98), Eurostat’s collection Theme 9: Research and development, pp. 105-117.


Journal papers in applied fields

  1. Michel, S., Swingedouw, D., Chavent, M., Ortega, P., Mignot, J., and Khodri, M. (2020). Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0. Geosci. Model Dev., Vol 13, pp. 841–858.
  2. Carrion‐Castillo, A., Van der Haegen, L., Tzourio‐Mazoyer, N., Kavaklioglu, T., Badillo, S.,Chavent, M., Jérôme Saracco, J., Brysbaert, M., Fisher, S.E.,vMazoyer, B., Francks, C. (2019). Genome sequencing for rightward hemispheric language dominance. Genes, Brain and Behavior, DOI:10.1111/gbb.12572.
  3. Conanec, A., Picard, B., Durand, D., Cantalapiedra-Hijar, G., Chavent, M., Denoyelle, C., Gruffat, D., Normand, J., Saracco, J.,, Ellies-Oury, M.P. (2019). New Approach Studying Interactions Regarding Trade-Off between Beef Performances and Meat Qualities. Foods, Vol 8, No 6, 197, doi:10.3390/foods8060197.
  4. Partiot, C., Bessou, M., Chavent, M., Dodré, E. Maureille, B., Thomas, A. (2017). Identification des cas de trépanations dans les populations anciennes : base de données et outil interactif de soutien au diagnostic différentiel. BMSAP, Vol 29, No 3-4, pp. 185-194.
  5. Ellies-Oury M.P., Gagaoua M., Saracco J., Chavent M., Picard B. (2017). Biomarker Abundance in Two Beef Muscles Depending on Animal Breeding Practices and Carcass Characteristics. J Bioinform, Genomics, Proteomics, Vol 2, No 1, pp. 1013-.
  6. Ellies-Ourya,M.P., Cantalapiedra-Hijarb, G., Durand, D., Gruffat, D., Listrat, A., Micol, D., Ortigues-Marty, I., Hocquette, J.-F., Chavent, M., Saracco,  J., Picard, B. (2016). An innovative approach combining animal performances, nutritional value and sensory quality of meat. Meat Science, Vol 122, pp. 163-172.
  7. Bessieux-Ollier, C., Chavent, M., Kuentz and V., Walliser, E. (2012). The mandatory adoption of IFRS on intangibles: upheaval or inertia? The case of France. Int. J. Accounting. Auditing and Performance Evaluation, Vol. 8, pp. 91-113.
  8. Bessieux-Ollier, C., Chavent, M., Kuentz, V. and Walliser, E. (2010). L’adoption en France des normes IFRS relatives aux incorporels. Revue française de gestion – N° 207/2010, pp. 93-110.
  9. Guégan, H., Leminh, Q., Chavent, M., Patouille, B. and Bourquin P. (2008). Identification et Quantification des contributions relatives des sources de poussières fines en milieu urbain. Pollution atmosphérique, n° 198-199, 197-204.
  10. Chavent, M., Ding, Y., Fu, L., Stolowy, H. and Wang, H. (2006). Disclosure and determinants studies: An extension using the divisive clustering method. European Accounting Review, Vol. 15, pp. 181-218.
  11. Mongrand, S., Badoc, A., Patouille, B., Lacomblez, C., Chavent and M., Bessoule J.J. (2005). Chemotaxonomy of the Rubiaceae family based on leaf fatty acid composition. Phytochemistry, Vol. 66, pp. 549-559.
  12. Lacomblez, C., Chavent, M. and Patouille, B. (2004). Analyse des données statistiques en zone méditerranéenne. Préventique Sécurité, n°77, pp. 8-11.
  13. Mongrand, S., Badoc, A., Patouille, B., Lacomblez, C., Chavent, M. and Cassagne, C. (2001). Taxonomy of gymnospermae: multivariate analyses of leaf fatty acid composition. Phytochemistry, Vol. 58, pp. 101-115.


Book chapters

  1. Chavent, M., Brito, P. (2022). Divisive Clustering of Histogram Data. In Analysis of Distributional Data, P.Brito & S. Dias Eds., CRC Press, pp 127-138.
  2. Saracco, J., Chavent, M. (2016). Clustering of Variables for Mixed Data. In Statistics for Astrophysics: Clustering and Classification, EAS Publications Series, 77, EDP Sciences,  pp. 91-119.
  3. Legrand, P., Vezard, L.,  Chavent, M., Faita-Ainseba, F., Trujillo, L. (2014). Feature extraction and classification of EEG signals. The use of a genetic algorithm for an application on alertness prediction. In: Guide to Brain-Computer Music Interfacing, E. Miranda; J. Castet, B. Knapp (editors), pp. 191-220, Springer.
  4. Chavent, M. (2007). Species Clustering via Classical and Interval Data Representation. In Selected contributions in data analysis and classification, Brito, P., Cucumel, G., Bertrand, P., Carvahlo, F. Eds., pp. 183-191, Springer.
  5. Chavent, M. (2000). Criterion-Based Divisive Clustering for Symbolic Objects. In: Analysis of symbolic data, Bock, H.-H., Diday,  E. Eds., pp. 299-311, Springer.
  6. Chavent, M. and Bock H.H. (2000). Clustering Problem, Clustering Methods for Classical Data. In: Analysis of symbolic data, Bock, H.-H., Diday,  E. Eds., pp. 294-299, Springer.