Bibliography

Major publications by the team in recent years

• 1S. Arlot, A. Celisse.
  
  Segmentation of the mean of heteroscedastic data via cross-validation, in: Statistics and Computing, 2010, vol. 21, pp. 613–632.
• 2P. Bathia, S. Iovleff, G. Govaert.
  
  An R Package and C++ library for Latent block models: Theory, usage and applications, in: Journal of Statistical Software, 2016.
  
  https://hal.archives-ouvertes.fr/hal-01285610
• 3C. Biernacki, G. Celeux, G. Govaert.
  
  Exact and Monte Carlo Calculations of Integrated Likelihoods for the Latent Class Model, in: Journal of Statistical Planning and Inference, 2010, vol. 140, pp. 2991-3002.
  
  https://hal.archives-ouvertes.fr/hal-00554344
• 4C. Biernacki, J. Jacques.
  
  A generative model for rank data based on an insertion sorting algorithm, in: Computational Statistics and Data Analysis, 2013, vol. 58, pp. 162-176. [ DOI : 10.1016/j.csda.2012.08.008 ]
  
  https://hal.archives-ouvertes.fr/hal-00441209
• 5C. Biernacki, J. Jacques.
  
  Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm, in: Statistics and Computing, 2016, vol. 26, n^o 5, pp. 929-943.
  
  https://hal.inria.fr/hal-01052447
• 6A. Celisse, J.-J. Daudin, L. Pierre.
  
  Consistency of maximum likelihood and variational estimators in stochastic block model, in: Electronic Journal of Statistics, 2012, pp. 1847–1899.
  
  http://projecteuclid.org/handle/euclid.ejs
• 7M. Giacofci, S. Lambert-Lacroix, G. Marot, F. Picard.
  
  Wavelet-based clustering for mixed-effects functional models in high dimension, in: Biometrics, March 2013, vol. 69, n^o 1, pp. 31-40. [ DOI : 10.1111/j.1541-0420.2012.01828.x ]
  
  http://hal.inria.fr/hal-00782458
• 8J. Jacques, C. Preda.
  
  Funclust: a curves clustering method using functional random variables density approximation, in: Neurocomputing, 2013, vol. 112, pp. 164-171.
  
  https://hal.archives-ouvertes.fr/hal-00628247
• 9M. Marbac, C. Biernacki, V. Vandewalle.
  
  Model-based clustering of Gaussian copulas for mixed data, in: Communications in Statistics - Theory and Methods, December 2016.
  
  https://hal.archives-ouvertes.fr/hal-00987760
• 10V. Vandewalle, C. Biernacki, G. Celeux, G. Govaert.
  
  A predictive deviance criterion for selecting a generative model in semi-supervised classification, in: Computational Statistics and Data Analysis, 2013, vol. 64, pp. 220-236.
  
  https://hal.inria.fr/inria-00516991

Publications of the year

Articles in International Peer-Reviewed Journals

• 11M.-S. Ahmed, M. K. Attouch, S. Dabo-Niang.
  
  Binary functional linear models under choice-based sampling, in: Econometrics and Statistics , July 2017. [ DOI : 10.1016/j.ecosta.2017.07.001 ]
  
  https://hal.inria.fr/hal-01654079
• 12P. Alquier, B. Guedj.
  
  An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization, in: Mathematical Methods of Statistics, 2017.
  
  https://hal.inria.fr/hal-01251878
• 13P. Alquier, B. Guedj.
  
  Simpler PAC-Bayesian Bounds for Hostile Data, in: Machine Learning, 2017, forthcoming.
  
  https://hal.inria.fr/hal-01385064
• 14E. Chazard, G. Ficheur, J.-B. Beuscart, C. Preda.
  
  How to Compare the Length of Stay of Two Samples of Inpatients? A Simulation Study to Compare Type I and Type II Errors of 12 Statistical Tests, in: Value in Health, July 2017, vol. 20, n^o 7, pp. 992 - 998. [ DOI : 10.1016/j.jval.2017.02.009 ]
  
  https://hal.archives-ouvertes.fr/hal-01655463
• 15S. Dabo-Niang, A. Amiri.
  
  Density estimation over spatio-temporal data streams, in: Econometrics and Statistics , September 2017. [ DOI : 10.1016/j.ecosta.2017.08.005 ]
  
  https://hal.inria.fr/hal-01654081
• 16J. Dubois, V. Dubois, H. Dehondt, P. Mazrooei, C. Mazuy, A. A. Sérandour, C. Gheeraert, P. Guillaume, E. Baugé, B. Derudas, N. Hennuyer, R. Paumelle, G. Marot, J. S. Carroll, M. Lupien, B. Staels, P. Lefebvre, J. Eeckhoute.
  
  The logic of transcriptional regulator recruitment architecture at cis -regulatory modules controlling liver functions, in: Genome Research, June 2017, vol. 27, n^o 6, pp. 985 - 996. [ DOI : 10.1101/gr.217075.116 ]
  
  https://hal.archives-ouvertes.fr/hal-01647846
• 17E. Hebbinckuys, J.-P. Marissal, C. Preda, V. Leclercq.
  
  Assessing the burden of Clostridium difficile infections for hospitals, in: Journal of Hospital Infection, September 2017, pp. 1-7. [ DOI : 10.1016/j.jhin.2017.08.023 ]
  
  https://hal.archives-ouvertes.fr/hal-01655460
• 18M. Marbac, C. Biernacki, V. Vandewalle.
  
  Model-based clustering of Gaussian copulas for mixed data, in: Communications in Statistics - Theory and Methods, 2017, vol. 46, n^o 23, pp. 11635-11656.
  
  https://hal.archives-ouvertes.fr/hal-00987760
• 19C. Preda, A. Dermoune.
  
  Parametrizations, fixed and random effects, in: Journal of Multivariate Analysis, February 2017, vol. 154, pp. 162 - 176. [ DOI : 10.1016/j.jmva.2016.11.001 ]
  
  https://hal.archives-ouvertes.fr/hal-01655461

Invited Conferences

• 20C. Biernacki, A. Lourme, M. Brunin, A. A. Celisse.
  
  About Two Disinherited Sides of Statistics: Data Units and Computational Saving, in: Statlearn 2017, Lyon, France, April 2017, pp. 1-56.
  
  https://hal.inria.fr/hal-01665905
• 21V. Vandewalle, C. Biernacki.
  
  Dealing with missing data through mixture models, in: ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”, Varsovie, Poland, May 2017, pp. 1-3.
  
  https://hal.inria.fr/hal-01667614
• 22V. Vandewalle, C. Biernacki.
  
  Survival analysis with complex covariates: a model-based clustering preprocessing step, in: IEEE PHM 2017, Dallas, United States, June 2017.
  
  https://hal.inria.fr/hal-01667588
• 23V. Vandewalle, T. Mottet, M. Marbac.
  
  Model-based variable clustering, in: CMStatistics/ERCIM 2017 - 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom, December 2017, pp. 1-19.
  
  https://hal.inria.fr/hal-01691421
• 24V. Vandewalle.
  
  Simultaneous dimension reduction and multi-objective clustering, in: IFCS 2017 - Conference of the International Federation of Classification Societies, Tokyo, Japan, August 2017, pp. 1-29.
  
  https://hal.inria.fr/hal-01662271

Conferences without Proceedings

• 25M. Baelde, C. Biernacki, R. Greff.
  
  A mixture model-based real-time audio sources classification method, in: The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2017, New Orleans, United States, March 2017.
  
  https://hal.archives-ouvertes.fr/hal-01420677
• 26M. Baelde, C. Biernacki, R. Greff.
  
  Classification de signaux audio en temps-réel par un modèle de mélanges d'histogrammes, in: JDS 2017 - 49e Journées de Statistiques, Avignon, France, May 2017.
  
  https://hal.archives-ouvertes.fr/hal-01592496
• 27C. Biernacki, A. Lourme.
  
  Introduction Units in model-based clustering Units in model-based co-clustering Conclusion Unifying Data Units and Models in (Co-)Clustering, in: 2017 - Classification Society Conference, Santa Clara, United States, June 2017, pp. 1-38.
  
  https://hal.archives-ouvertes.fr/hal-01653896
• 28C. Biernacki, A. Lourme.
  
  Units in model-based clustering Units in model-based co-clustering , in: 24e rencontres de la Société Francophone de Classification, Lyon, France, June 2017.
  
  https://hal.archives-ouvertes.fr/hal-01653899
• 29M. Brunin, C. Biernacki, A. Celisse.
  
  Compromis précision - temps de calcul appliqué au problème de régression linéaire, in: 2017 - 49e Journées de Statistique de la SFdS, Avignon, France, May 2017, pp. 1-6.
  
  https://hal.archives-ouvertes.fr/hal-01653754
• 30F. Chamroukhi, C. Biernacki.
  
  Model-Based Co-Clustering of Multivariate Functional Data, in: ISI 2017 - 61st World Statistics Congress, Marrakech, Morocco, July 2017.
  
  https://hal.archives-ouvertes.fr/hal-01653782
• 31A. Ehrhardt, C. Biernacki, V. Vandewalle, P. Heinrich, S. Beben.
  
  Réintégration des refusés en Credit Scoring, in: 49e Journées de Statistique, Avignon , France, May 2017.
  
  https://hal.archives-ouvertes.fr/hal-01653767
• 32S. Iovleff, M. Fauvel, S. Girard, C. Preda, V. Vandewalle.
  
  Mixture Models with Missing data Classication of Satellite Image Time Series: QUALIMADOS: Atelier Qualité des masses de données scientiques, in: Journées Science des Données MaDICS 2017, Marseille, France, June 2017, pp. 1-60.
  
  https://hal.archives-ouvertes.fr/hal-01649206

Scientific Books (or Scientific Book chapters)

• 33C. Biernacki.
  
  Mixture models, in: Choix de modèles et agrégation, J.-J. Droesbeke, G. Saporta, C. Thomas-Agnan (editors), Technip, September 2017.
  
  https://hal.inria.fr/hal-01252671
• 34C. Biernacki, C. Maugis.
  
  High-dimensional clustering, in: Choix de modèles et agrégation, Sous la direction de J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition: Technip, September 2017.
  
  https://hal.archives-ouvertes.fr/hal-01252673

Other Publications

• 35M. Baelde, C. Biernacki, R. Greff.
  
  Real-time Audio Classification based on Mixture Models, March 2017, The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017), Poster.
  
  https://hal.archives-ouvertes.fr/hal-01481934
• 36C. Biernacki, A. Lourme.
  
  Unifying Data Units and Models in (Co-)Clustering, December 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01653881
• 37S. Bouka, S. Dabo-Niang, G. M. Nkiet.
  
  On estimation in a spatial functional linear regression model with derivatives, March 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01516616
• 38B. Guedj, B. Srinivasa Desikan.
  
  Pycobra: A Python Toolbox for Ensemble Learning and Visualisation, April 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01514059
• 39A. Hiba, N. Wicker, C. Biernacki.
  
  Projection under pairwise distance controls, December 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01420662
• 40J. Jacques, C. Biernacki.
  
  Model-Based Co-clustering for Ordinal Data, January 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01448299
• 41M. Marbac, V. Vandewalle.
  
  A tractable Multi-Partitions Clustering, January 2018, https://arxiv.org/abs/1801.07063 - working paper or preprint.
  
  https://hal.inria.fr/hal-01691417
• 42M. Selosse, J. Jacques, C. Biernacki, F. Cousson-Gélie.
  
  Analyzing health quality survey using constrained co-clustering model for ordinal data and some dynamic implication, November 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01643910
• 43M. Selosse, J. Jacques, C. Biernacki.
  
  ordinalClust: a package for analyzing ordinal data, January 2018, working paper or preprint.
  
  https://hal.inria.fr/hal-01678800