Bibliography

Major publications by the team in recent years

• 1A. Amarioarei, C. Preda.
  
  Approximation for the Distribution of Three-dimensional Discrete Scan Statistic, in: Methodology and Computing in Applied Probability, September 2013, 14 p. [ DOI : 10.1007/s11009-013-9382-3 ]
  
  https://hal.inria.fr/hal-01092992
• 2S. Arlot, A. Celisse.
  
  Segmentation of the mean of heteroscedastic data via cross-validation, in: Statistics and Computing, 2010, vol. 21, pp. 613–632.
• 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
• 5A. 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
• 6M. 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
• 7J. Jacques, C. Biernacki.
  
  Extension of model-based classification for binary data when training and test populations differ, in: Journal of Applied Statistics, 2010, vol. 37, n^o 5, pp. 749-766.
  
  https://hal.archives-ouvertes.fr/hal-00316080
• 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
• 9A. Lourme, C. Biernacki.
  
  Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins, in: Computational Statistics, December 2013, vol. 152, n^o 3, pp. 371-391.
  
  https://hal.inria.fr/hal-00921041
• 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

Doctoral Dissertations and Habilitation Theses

• 11C. Théry.
  
  Model-based covariable decorrelation in linear regression (CorReg). Application to missing data and to steel industry, Université Lille 1, July 2015.
  
  https://hal.archives-ouvertes.fr/tel-01249789

Articles in International Peer-Reviewed Journals

• 12C. Biernacki, J. Jacques.
  
  Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm, in: Statistics and Computing, June 2015.
  
  https://hal.inria.fr/hal-01052447
• 13C. Bouveyron, E. Côme, J. Jacques.
  
  The discriminative functional mixture model for a comparative analysis of bike sharing systems, in: The Annals of Applied Statistics, 2015, forthcoming.
  
  https://hal.archives-ouvertes.fr/hal-01024186
• 14S. Dabo-Niang, A. Laksaci, Z. Kaid.
  
  On spatial conditional quantile estimation for a functional regressor, in: AStA Advances in Statistical Analysis, February 2015.
  
  https://hal.inria.fr/hal-01206774
• 15S. Dabo-Niang, G.-M. Nkiet, S. Bouka.
  
  Non-parametric level set estimation for spatial data, in: Advances and Applications in Statistics, September 2015, vol. 46, n^o 2, pp. 119 - 158. [ DOI : 10.17654/ADASAug2015_119_158 ]
  
  https://hal.inria.fr/hal-01206787
• 16S. Dabo-Niang, C. Ternynck, F. Chebana, M. Ali Ben Alaya, T. Ouarda.
  
  Streamflow hydrograph classification using functional data analysis, in: Journal of Hydrometeorology, October 2015. [ DOI : 10.1175/JHM-D-14-0200.1 ]
  
  https://hal.inria.fr/hal-01206807
• 17S. Dabo-Niang, A.-F. Yao, C. Ternynck.
  
  A new spatial regression estimator in the multivariate context, in: Comptes rendus de l'académie des sciences, Mathématiques, April 2015. [ DOI : 10.1016/j.crma.2015.04.004 ]
  
  https://hal.inria.fr/hal-01206781
• 18S. Iovleff.
  
  Probabilistic Auto-Associative Models and Semi-Linear PCA, in: Advances in Data Analysis and Classification, September 2015, vol. 9, n^o 3, 20 p. [ DOI : 10.1007/s11634-014-0185-3 ]
  
  https://hal.archives-ouvertes.fr/hal-00734070
• 19R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux, G. Govaert.
  
  Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library, in: Journal of Statistical Software, 2015, forthcoming.
  
  https://hal.archives-ouvertes.fr/hal-00919486
• 20M. Marbac, C. Biernacki, V. Vandewalle.
  
  Model-based clustering for conditionally correlated categorical data, in: Journal of Classification, 2015, vol. 2, n^o 32, pp. 145-175. [ DOI : 10.1007/s00357 ]
  
  https://hal.inria.fr/hal-00787757
• 21S. Poulain, C. Roumier, A. Venet-Caillault, M. Figeac, C. Herbaux, G. Marot, E. DOYE, E. Bertrand, S. GEFFROY, F. LEPRETRE, O. Nibourel, A. DECAMBRON, E. Boyle, A. Renneville, S. Tricot, A. Daudignon, B. Quesnel, P. Duthilleul, C. Preudhomme, X. Leleu.
  
  Genomic landscape of CXCR4 mutations in Waldenstrom's Macroglobulinemia, in: Clinical Cancer Research, October 2015, vol. 21, n^o 22. [ DOI : 10.1158/1078-0432.CCR-15-0646 ]
  
  https://hal.inria.fr/hal-01230058

Articles in National Peer-Reviewed Journals

• 22K. Benabdeslem, C. Biernacki, M. Lebbah.
  
  Les trois défis du Big-Data: Eléments de reflexion, in: Statistique et Société, 2015, vol. 3, n^o 1, pp. 19-22.
  
  https://hal.archives-ouvertes.fr/hal-01198435

Invited Conferences

• 23C. Biernacki.
  
  Clustering: evolution of methods to meet new challenges, in: Journée thématique Clustering, Issy Les Moulineaux, France, Orange Labs, October 2015.
  
  https://hal.archives-ouvertes.fr/hal-01253394
• 24C. Biernacki.
  
  MixtComp software: Model-based clustering/imputation with mixed data, missing data and uncertain data, in: MISSDATA 2015, Rennes, France, June 2015.
  
  https://hal.archives-ouvertes.fr/hal-01253393
• 25C. Biernacki, T. Deregnaucourt, V. Kubicki.
  
  Model-based clustering with mixed/missing data using the new software MixtComp, in: CMStatistics 2015 (ERCIM 2015), London, United Kingdom, December 2015.
  
  https://hal.archives-ouvertes.fr/hal-01249829
• 26S. Dabo-Niang.
  
  Spatial risk estimation and application to biomedical data, in: Tunisian Association of Statistics and Applications Conference, Hammamet, Tunisia, March 2015.
  
  https://hal.inria.fr/hal-01206865
• 27S. Dabo-Niang.
  
  Spatial Risk estimation and Applications, in: African Women in Mathematics Association Conference, Navaisha, Kenya, July 2015.
  
  https://hal.inria.fr/hal-01206866
• 28J. Jacques.
  
  Clustering multivariate ranking data, in: 60th World Statistics Congress – ISI2015, Rio de Janeiro, Brazil, July 2015.
  
  https://hal.inria.fr/hal-01241256
• 29J. Jacques.
  
  The discriminative functional mixture model for a comparative analysis of bike sharing systems, in: CMStatistics 2015, 8th International Conference of the ERCIM working group on Computational and Methodological Statistics, London, United Kingdom, December 2015.
  
  https://hal.inria.fr/hal-01241254
• 30F. Loingeville, J. Jacques, C. Preda, P. Guarini, O. Molinier.
  
  Modèle Linéaire Généralisé Hiérarchique Gamma-Poisson à 3 facteurs aléatoires - Application au contrôle de qualité, in: 47èmes Journées de Statistique, Lille, France, Société Française de Statistique, June 2015.
  
  https://hal.archives-ouvertes.fr/hal-01152840
• 31M. Marbac, C. Biernacki, V. Vandewalle.
  
  Model-based clustering of categorical data by relaxing conditional independence, in: Classification Society Meeting, Hamilton, Ontario, Canada, Mc Master University, June 2015.
  
  https://hal.inria.fr/hal-01238334
• 32C. Preda.
  
  Regression with categorical functional data, in: 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, Londres, United Kingdom, December 2015.
  
  https://hal.inria.fr/hal-01251289
• 33V. Vandewalle, C. Biernacki.
  
  An efficient SEM algorithm for Gaussian Mixtures with missing data, in: 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, Londres, United Kingdom, December 2015.
  
  https://hal.inria.fr/hal-01242588
• 34V. Vandewalle, C. Cozma, C. Preda.
  
  Clustering categorical functional data Application to medical discharge letters, in: 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, Londres, France, December 2015.
  
  https://hal.inria.fr/hal-01251284

International Conferences with Proceedings

• 35C. Bouveyron, J. Jacques.
  
  Un algorithme EM pour une version parcimonieuse de l’analyse en composantes principales probabiliste, in: EGC 2015 - 15ème conférence internationale sur l'extraction et la gestion des connaissances, Luxembourg, Luxembourg, January 2015.
  
  https://hal.inria.fr/hal-01241262
• 36M. A. Hasnat, J. VELCIN, S. Bonnevay, J. Jacques.
  
  Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study, in: Intelligent Data Analysis, Saint Etienne, France, October 2015.
  
  https://hal.inria.fr/hal-01203561

Conferences without Proceedings

• 37M. Brunin, C. Biernacki, A. Celisse.
  
  Compromis précision-temps de calcul et détection de ruptures, in: 6ème Rencontres des Jeunes Statisticiens, Le Teich, France, August 2015.
  
  https://hal.inria.fr/hal-01238276
• 38Q. Grimonprez, A. Celisse, G. Marot.
  
  Sélection de groupes de variables corrélées par classification ascendante hiérarchique et group-lasso, in: Sixièmes rencontres des jeunes statisticiens, Le Teich, France, SFdS, August 2015.
  
  https://hal.inria.fr/hal-01238253
• 39Q. Grimonprez, A. Celisse, G. Marot.
  
  Sélection de groupes de variables corrélées par classification ascendante hiérarchique et group-lasso, in: 47èmes Journées de Statistique, Lille, France, June 2015.
  
  https://hal.inria.fr/hal-01238248
• 40S. Iovleff.
  
  Rtkpp: Un package pour faire l'interface entre R et la bibliothèque STK++, in: Quatrièmes Rencontres R, Grenoble, France, June 2015.
  
  https://hal.inria.fr/hal-01253792

Scientific Books (or Scientific Book chapters)

• 41C. Biernacki.
  
  Mixture models, in: Choix de modèles et agrégation, J.-J. Droesbeke, G. Saporta, C. Thomas-Agnan (editors), Technip, December 2015.
  
  https://hal.inria.fr/hal-01252671
• 42C. 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, December 2015.
  
  https://hal.archives-ouvertes.fr/hal-01252673

Other Publications

• 43P. Alquier, B. Guedj.
  
  Bayesian Non-Negative Matrix Factorization, January 2016, working paper or preprint.
  
  https://hal.inria.fr/hal-01251878
• 44A. Celisse, T. Mary-Huard.
  
  New upper bounds on cross-validation for the k-Nearest Neighbor classification rule, August 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01185092
• 45S. Dabo-Niang, A. Amiri, M. Yahaya.
  
  Non-parametric recursive density estimation for space-time data-stream, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206833
• 46S. Dabo-Niang, A. Amiri, M. Yahaya.
  
  Non-parametric recursive density estimation for spatial data, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206814
• 47S. Dabo-Niang, A. Bassene, A. Diop.
  
  Conditional tail index estimation for random fields: fixed design case, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206824
• 48S. Dabo-Niang, A. Bassene, A. Diop.
  
  Kernel estimation of conditional tail index and quantile estimation for random fields, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206829
• 49S. Dabo-Niang, A. Diop, A. Bassene.
  
  A note on conditional tail index estimation for random fields, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206809
• 50S. Dabo-Niang, B. Thiam.
  
  Nonparametric estimation of a regression function for spatial data with errors, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01206832
• 51C. Friguet, F. Letué, V. Vandewalle.
  
  Table ronde : “pourquoi et comment enseigner l’analyse de données massives (big data)”, June 2015, 47èmes Journées de Statistique de la SFdS.
  
  https://hal.inria.fr/hal-01250812
• 52B. Guedj, S. Robbiano.
  
  PAC-Bayesian High Dimensional Bipartite Ranking, November 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01226472
• 53J. Hamon, G. Even, R. Dassonneville, J. Jacques, C. Dhaenens.
  
  Use of a novel evolutionary algorithm for genomic selection, January 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01100660
• 54M. A. Hasnat, J. VELCIN, S. Bonnevay, J. Jacques.
  
  Opinion mining from Twitter data using evolutionary multinomial mixture models, September 2015, working paper or preprint.
  
  https://hal.inria.fr/hal-01204613
• 55J. Kellner, A. Celisse.
  
  A One-Sample Test for Normality with Kernel Methods, July 2015, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01175237
• 56L. Yengo, J. Jacques, C. Biernacki, M. Canouil.
  
  Variable Clustering in High-Dimensional Linear Regression: The R Package clere, October 2015, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-00940929