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, pp. 1–20.
  http://www.springerlink.com/content/jq202v115512u26p/
• 2S. Arlot, A. Celisse, Z. Harchaoui.
  Kernel change-point detection, 2012.
• 3C. Biernacki.
  Pourquoi les modèles de mélange pour la classification ?, in: La Revue de Modulad, 2009, vol. 40, pp. 1–22.
• 4C. Biernacki, G. Celeux, G. Govaert.
  Exact and Monte Carlo Calculations of Integrated Likelihoods for the Latent Class Model, in: Journal of Statistical and Planning Inference, 2010, n^o 1, pp. 2991–3002.
• 5C. Biernacki, J. Jacques.
  A generative model for rank data based on sorting algorithm, in: Computational Statistics and Data Analysis, 2013, n^o 58, pp. 162–176.
• 6A. Biernacki.
  Gaussian Parsimonious Clustering Models Scale Invariant and Stable by Projection, in: Statistics and Computing, in press.
• 7A. Celisse.
  Optimal cross-validation in density estimation, ArXiv, 2013, n^o arXiv:0811.0802v3.
• 8A. 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
• 9S. Dabo-Niang, S. A. Ould-Abdi, A. Diop.
  Consistency of a nonparametric conditional mode estimator for random fields, in: Statistical Methods and Applications, 2013. [ DOI : 10.1007/s10260-013-0239-2 ]
• 10M. Giacofci, S. Lambert-Lacroix, G. Marot, F. Picard.
  Wavelet-based clustering for mixed-effects functional models in high dimension, in: Biometrics, 2012, to appear.
• 11M. Guedj, A. Celisse, G. Nuel.
  kerfdr: A semi-parametric kernel-based approach to local FDR estimations, in: BMC Bioinformatics, 2009, vol. 84, n^o 10, (electronic).
• 12J. 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.
• 13M. Marbac, C. Biernacki, V. Vandewalle.
  Modèle de classification de données qualitatives par modes de dépendance conditionnelle, in: 45e Journées de Statistique de la SFDS, Toulouse, 2013.
• 14M. Marbac, C. Biernacki, V. Vandewalle.
  Modèle de mélange de copules Gaussiennes pour la classification des données hétérogènes, in: Cinquièmes Rencontres des Jeunes Statisticien-ne-s, Aussois, 2013.
• 15M. Marbac, C. Biernacki, V. Vandewalle.
  Model-based clustering for conditionally correlated categorical data, Inria, 2013, n^o RR-8232.
• 16V. 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, in press.
• 17V. Vandewalle, C. Biernacki, M. Marbac.
  Modèle de classification de données qualitatives par modes de dépendance conditionnelle, in: Seminar of probability and statistics, Paris V, 2013.

Publications of the year

Articles in International Peer-Reviewed Journals

• 18C. Biernacki, A. Lourme.
  Gaussian Parsimonious Clustering Models Scale Invariant and Stable by Projection, in: Statistics and Computing, December 2013, In press.
  http://hal.inria.fr/hal-00688250
• 19S. Dabo-Niang, S. Ali Ould Abdi, A. Ould Abdi, A. Diop.
  Consistency of a nonparametric conditional mode estimator for random fields, in: Statistical Methods and Applications, 2013. [ DOI : 10.1007/s10260-013-0239-2 ]
  http://hal.inria.fr/hal-00921178
• 20S. Dabo-Niang, A.-F. Yao.
  Spatial kernel density estimation for functional random variables, in: Metrika, 2013, vol. 1, pp. 19-52.
  http://hal.inria.fr/hal-00943638
• 21E. Eirola, A. Lendasse, V. Vandewalle, C. Biernacki.
  Mixture of Gaussians for Distance Estimation with Missing Data, in: Neurocomputing, December 2013, vol. In press.
  http://hal.inria.fr/hal-00921023
• 22M. 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
• 23J. Jacques, C. Preda.
  Funclust: a curves clustering method using functional random variables density approximation, in: Neurocomputing, 2013, vol. 112, pp. 164-171.
  http://hal.inria.fr/hal-00628247
• 24J. Jacques, C. Preda.
  Functional data clustering: a survey, in: Advances in Data Analysis and Classification, January 2013, 25 p. [ DOI : 10.1007/s11634-013-0158-y ]
  http://hal.inria.fr/hal-00771030
• 25J. Jacques, C. Preda.
  Model-based clustering for multivariate functional data, in: Computational Statistics and Data Analysis, 2014, vol. 71, pp. 92-106.
  http://hal.inria.fr/hal-00713334
• 26A. 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.
  http://hal.inria.fr/hal-00921041
• 27R. Walker, M. Gissot, L. Huot, T. Dilezitoko Alayi, D. Hot, G. Marot, C. Schaeffer-Reiss, A. Van Dorsselaer, K. Kim, S. Tomavo.
  Toxoplasma transcription factor TgAP2XI-5 regulates the expression of genes involved in parasite virulence and host invasion, in: Journal of Biological Chemistry, 2013.
  http://hal.inria.fr/hal-00921150
• 28L. Yengo, J. Jacques, C. Biernacki.
  Variable clustering in high dimensional linear regression models, in: Journal de la Société Française de Statistique, 2014, in press.
  http://hal.inria.fr/hal-00764927

Invited Conferences

• 29S. Dabo-Niang.
  Spatial Data Analysis, in: 8th International conference of Sousse ISG, Sousse, Tunisia, 2013.
  http://hal.inria.fr/hal-00943641
• 30J. Jacques.
  Clustering multivariate ordinal data, in: 20th Summer Working Group on Model-Based Clustering of the Department of Statistics of the University of Washington, Bologna, Italy, July 2013.
  http://hal.inria.fr/hal-00943733
• 31J. Jacques.
  Model-based clustering for multivariate functional data, in: ERCIM 2013, 6th International Conference of the ERCIM working group on Computational and Methodological Statistics, London, United Kingdom, December 2013.
  http://hal.inria.fr/hal-00943732

International Conferences with Proceedings

• 32S. Dabo-Niang.
  Exploring spatial non-parametric estimations, in: Marrakesh International Conference on Probability and Statistics, Marrakech, Morocco, 2013.
  http://hal.inria.fr/hal-00943642

Conferences without Proceedings

• 33Q. Grimonprez, J. Jacques, C. Biernacki.
  Rankclust: An R package for clustering multivariate partial rankings, in: Deuxième Rencontres R, France, 2013.
  http://hal.inria.fr/hal-00944005
• 34J. Hamon, C. Dhaenens, G. Even, J. Jacques.
  Feature selection in high dimensional regression problems for genomic, in: Tenth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Nice, France, June 2013.
  http://hal.inria.fr/hal-00839705
• 35J. Hamon, C. Dhaenens, G. Even, J. Jacques.
  Modèles mixtes en génétique animale : sélection de variables par optimisation combinatoire, in: 45ème Journées De Statistiques, Toulouse, France, May 2013.
  http://hal.inria.fr/hal-00839707

Internal Reports

• 36M. Attouch, M. Salem Ahmed, S. Dabo-Niang, A. Diop.
  k-nearest neighbors method estimation of regression function for spatial dependent data, 2014.
  http://hal.inria.fr/hal-00943647
• 37S. Bouka, S. Dabo-Niang, G. Gayraud, G.-M. Nkiet.
  Minimax testing in a spatial discrete regression scheme, 2014.
  http://hal.inria.fr/hal-00943645
• 38S. Dabo-Niang, L. Hamdad, C. Ternynck, A.-F. Yao.
  A kernel spatial density estimation with applications to spatial clustering and Monsoon Asia Drought Atlas analysis, 2013.
  http://hal.inria.fr/hal-00943643
• 39S. Dabo-Niang, C. Ternynck, A.-F. Yao.
  A new spatial regression estimator in the multivariate context, 2014.
  http://hal.inria.fr/hal-00943646
• 40J. Kellner, A. Celisse.
  New goodness-of-fit tes for normality in RKHS, 2014.
  http://hal.inria.fr/hal-00943669
• 41M. Marbac, C. Biernacki, V. Vandewalle.
  Model-based clustering for conditionally correlated categorical data, Inria, February 2013, n^o RR-8232, 33 p.
  http://hal.inria.fr/hal-00787757
• 42C. Ternynck, M. Ali Ben Alaya, F. Chebana, S. Dabo-Niang, T. Ouarda.
  Flood hydrograph classification using functional data analysis, 2013.
  http://hal.inria.fr/hal-00943644

Patents

• 43M. Pierre-Jean, G. Marot, R. Guillem, A. Celisse.
  Change-point detection with kernel methods : application to DNA copy number signals, 2013.
  http://hal.inria.fr/hal-00943413
• 44M. Pierre-Jean, G. Marot, G. Rigaill, A. Celisse.
  Détection de ruptures à partir de méthodes à noyaux, 2013.
  http://hal.inria.fr/hal-00943423

Other Publications

• 45Q. Grimonprez, A. Celisse, M. Cheok, M. Figeac, G. Marot.
  MPAgenomics : An R package for multi-patients analysis of genomic markers, 2014.
  http://hal.inria.fr/hal-00933614
• 46J. Jacques, Q. Grimonprez, C. Biernacki.
  Rankcluster: An R package for clustering multivariate partial rankings, 2013.
  http://hal.inria.fr/hal-00840692
• 47M. Marbac, C. Biernacki, V. Vandewalle.
  Classification de données mixtes par un modèle de mélange de copules gaussiennes, 2014, 46e Journées de Statistique (Rennes, du 2 au 6 juin 2014 ).
  http://hal.inria.fr/hal-00940613
• 48A. Rau, G. Marot, F. Jaffrézic.
  Differential meta-analysis of RNA-seq data from multiple studies, June 2013.
  http://hal.inria.fr/hal-00834369
• 49L. Rémi, I. Serge, L. Florent, C. Biernacki, G. Celeux, G. Govaert.
  Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library, 2013.
  http://hal.inria.fr/hal-00919486
• 50L. Yengo, J. Jacques, C. Biernacki, M. Canouil.
  Variable Clustering in High-Dimensional Linear Regression: The R Package clere, 2013.
  http://hal.inria.fr/hal-00940929