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

• 11A. Amiri, S. Dabo-Niang, M. Yahaya.
  Non-parametric recursive density estimation for spatial data, in: Comptes Rendus Mathématique, 2016. [ DOI : 10.1016/j.crma.2015.10.010 ]
  https://hal.inria.fr/hal-01425935
• 12P. 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
• 13C. 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
• 14S. Dabo-Niang, S. Guillas, C. Ternynck.
  More efficient kernel functional regression estimation with correlated errors, in: Journal of Multivariate Analysis, 2016. [ DOI : 10.1016/j.jmva.2016.01.007 ]
  https://hal.inria.fr/hal-01425933
• 15S. Dabo-Niang, A. Maina, T. Soubdhan, H. Ould-Baba.
  Predictive spatio-temporal model for spatially sparse global solar radiation data, in: Energy, June 2016, vol. 111, pp. 599–608. [ DOI : 10.1016/j.energy.2016.06.004 ]
  https://hal.inria.fr/hal-01425930
• 16S. Dabo-Niang, C. Ternynck, A.-F. Yao.
  Nonparametric prediction in the multivariate spatial context, in: Journal of Nonparametric Statistics, 2016, vol. 28, n^o 2, pp. 428-458. [ DOI : 10.1080/10485252.2016.01.007 ]
  https://hal.inria.fr/hal-01425932
• 17A. Dermoune, C. Preda.
  Parametrizations, fixed and random effects, in: Journal of Multivariate Analysis, November 2016. [ DOI : 10.1016/j.jmva.2016.11.001 ]
  https://hal.archives-ouvertes.fr/hal-01424782
• 18C. Herbaux, E. Bertrand, G. Marot, C. Roumier, N. Poret, V. Soenen, O. Nibourel, C. Roche-Lestienne, N. Broucqsault, S. Galiègue-Zouitina, E. Boyle, G. Fouquet, A. Renneville, S. Tricot, F. Morschhauser, C. Preudhomme, B. Quesnel, S. Poulain, X. Leleu.
  BACH2 promotes indolent clinical presentation in Waldenström macroglobulinemia, in: Oncotarget, 2016.
  https://hal.inria.fr/hal-01423307
• 19J. Jacques, C. Ruckebusch.
  Model-based co-clustering for hyperspectral images, in: Journal of Spectral Imaging, 2016.
  https://hal.archives-ouvertes.fr/hal-01367941
• 20M. Marbac, C. Biernacki, V. Vandewalle.
  Latent class model with conditional dependency per modes to cluster categorical data, in: Advances in Data Analysis and Classification, June 2016, vol. 10, n^o 2, pp. 183–207. [ DOI : 10.1007/s11634-016-0250-1 ]
  https://hal.archives-ouvertes.fr/hal-00950112
• 21M. 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
• 22P. Masselot, S. Dabo-Niang, F. Chebana, T. B. Ouarda.
  Streamflow forecasting using functional regression, in: Journal of Hydrology, April 2016, vol. 538, pp. 754–766. [ DOI : 10.1016/j.jhydrol.2016.04.048 ]
  https://hal.inria.fr/hal-01425931
• 23C. Ternynck, M. Ali Ben Alaya, F. Chebana, S. Dabo-Niang, O. Taha.
  Streamflow hydrology classification using functional data analysis, in: Journal of Hydrometeorology, 2016. [ DOI : 10.1175/JHM-D-14-0200.1 ]
  https://hal.inria.fr/hal-01425934
• 24L. Yengo, J. Jacques, C. Biernacki, M. Canouil.
  Variable Clustering in High-Dimensional Linear Regression: The R Package clere, in: The R Journal, 2016, vol. 8, n^o 1, pp. 92-106.
  https://hal.archives-ouvertes.fr/hal-00940929

Invited Conferences

• 25H. Alawieh, N. Wicker, C. Biernacki.
  Projection under pairwise distance control, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016, ERCIM 2016), Séville, Spain, December 2016.
  https://hal.archives-ouvertes.fr/hal-01420681
• 26C. Biernacki.
  BigStat for Big Data: Big Data clustering through the BigStat SaaS platform, in: Journée scientifique « Big Data & Data science », Tunis, Tunisia, October 2016.
  https://hal.archives-ouvertes.fr/hal-01420650
• 27C. Biernacki, M. Brunin, A. Celisse.
  Computation time/accuracy trade-off and linear regression, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016, ERCIM 2016), Séville, Spain, December 2016.
  https://hal.archives-ouvertes.fr/hal-01420659
• 28C. Biernacki, G. Castellan, S. Chretien, B. Guedj, V. Vandewalle.
  Pitfalls in Mixtures from the Clustering Angle, in: Working Group on Model-Based Clustering Summer Session, Paris, France, July 2016.
  https://hal.archives-ouvertes.fr/hal-01419755
• 29C. Biernacki, A. Lourme.
  Unifying Data Units and Models in Statistics: Focus on (Co-)Clustering, in: Workshop on Model-based Clustering and Classification (MBC2), Catania, Italy, September 2016.
  https://hal.archives-ouvertes.fr/hal-01420657
• 30J. Jacques, C. Biernacki.
  Model-Based Co-clustering for Ordinal Data, in: Working Group on Model-Based Clustering Summer Session, Paris, France, July 2016.
  https://hal.archives-ouvertes.fr/hal-01420648
• 31J. Jacques, C. Biernacki.
  Model-based co-clustering for ordinal data, in: 23th Summer Working Group on Model-Based Clustering of the Department of Statistics of the University of Washington, Paris, France, July 2016.
  https://hal.inria.fr/hal-01383912

Conferences without Proceedings

• 32M. 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
• 33A.-L. Bedenel, C. Biernacki, L. Jourdan.
  Appariement de descripteurs evoluant en temps Application a la comparaison d'assurance en ligne, in: 48èmes Journées de Statistique de la SFdS, Montpellier, France, May 2016.
  https://hal.archives-ouvertes.fr/hal-01420667
• 34M. Brunin, C. Biernacki, A. Celisse.
  Compromis précision-temps de calcul appliqué au problèeme de détection de ruptures, in: 48èmes Journées de Statistique de la SFdS, Montpellier, France, May 2016.
  https://hal.archives-ouvertes.fr/hal-01420669
• 35J. Jacques, C. Biernacki.
  Model-based co-clustering for ordinal data, in: 48èmes Journées de Statistique organisée par la Société Française de Statistique, Montpellier, France, 2016.
  https://hal.archives-ouvertes.fr/hal-01383927
• 36J. Jacques, C. Ruckebusch.
  Co-clustering for hyperspectral images, in: 6th International Conference in Spectral Imaging, Chamonix, France, July 2016.
  https://hal.archives-ouvertes.fr/hal-01383918

Other Publications

• 37H. Alawieh, N. Wicker, C. Biernacki.
  Projection under pairwise distance controls, December 2016, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01420662
• 38P. Alquier, B. Guedj.
  An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization, August 2016, working paper or preprint.
  https://hal.inria.fr/hal-01251878
• 39P. Alquier, B. Guedj.
  Simpler PAC-Bayesian Bounds for Hostile Data, October 2016, working paper or preprint.
  https://hal.inria.fr/hal-01385064
• 40S. Arlot, A. Celisse, Z. Harchaoui.
  A kernel multiple change-point algorithm via model selection, March 2016, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-00671174
• 41A. Celisse, B. Guedj.
  Stability revisited: new generalisation bounds for the Leave-one-Out, August 2016, working paper or preprint.
  https://hal.inria.fr/hal-01355365
• 42A. Celisse, G. Marot, M. Pierre-Jean, G. Rigaill.
  New efficient algorithms for multiple change-point detection with kernels, September 2016, working paper or preprint.
  https://hal.inria.fr/hal-01413230
• 43S. Dabo-Niang, M. S. Ahmed.
  Generalized Functional Linear Models under Choice-Based Sampling, July 2016, working paper or preprint.
  https://hal.inria.fr/hal-01345918
• 44M. A. Hasnat, J. VELCIN, S. Bonnevay, J. Jacques.
  Evolutionary clustering for categorical data using parametric links among multinomial mixture models, March 2016, working paper or preprint.
  https://hal.inria.fr/hal-01204613
• 45L. Li, B. Guedj, S. Loustau.
  Clustering en ligne : le point de vue PAC-bayésien, January 2016, working paper or preprint.
  https://hal.inria.fr/hal-01264934
• 46L. Li, B. Guedj, S. Loustau.
  PAC-Bayesian Online Clustering, January 2016, working paper or preprint.
  https://hal.inria.fr/hal-01264233
• 47Y. B. Slimen, S. Allio, J. Jacques.
  Model-Based Co-clustering for Functional Data, December 2016, working paper or preprint.
  https://hal.inria.fr/hal-01422756
• 48V. Vandewalle, C. Preda.
  Clustering categorical functional data Application to medical discharge letters Medical discharge letters, July 2016, Working Group on Model-Based Clustering Summer Session: Paris, July 17-23, 2016, Poster.
  https://hal.inria.fr/hal-01424950
• 49V. Vandewalle.
  Simultaneous dimension reduction and multi-objective clustering using probabilistic factorial discriminant analysis, December 2016, CMStatistics 2016.
  https://hal.inria.fr/hal-01424965