Bibliography

• 1S. Arlot, A. Célisse.
  
  Segmentation of the mean of heteroscedastic data via cross-validation, in: Statistics and Computing, 2010, p. 1–20.
  
  http://www.springerlink.com/content/jq202v115512u26p/
• 2C. Biernacki.
  
  Pourquoi les modèles de mélange pour la classification ?, in: La Revue de Modulad, 2009, vol. 40, p. 1–22.
• 3C. 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, p. 2991–-3002.
• 4C. Bouveyron, J. Jacques.
  
  Adaptive linear models for regression: Improving prediction when population has changed, in: Pattern Recognition Letters, 2010, vol. 31, n^o 14, p. 2237–2247.
• 5S. Girard, S. Iovleff.
  
  Auto-associative models, nonlinear Principal component analysis, manifolds and projection pursuit, Principal Manifolds for Data Visualisation and Dimension Reduction, In A. Gorban et al, editors, 2007, vol. 8, LNCSE, Springer-Verlag.
• 6M. Guedj, A. Célisse, G. Nuel.
  
  kerfdr: A semi-parametric kernel-based approach to local FDR estimations, in: BMC Bioinformatics, 2009, vol. 84, n^o 10, (electronic).
• 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, p. 749–766.
• 8G. Marot, J.-L. Foulley, C.-D. Mayer, F. Jaffrézic.
  
  Moderated effect size and p-value combinations for microarray meta-analyses, in: Bioinformatics, 2009, vol. 25, n^o 20, p. 2692–2699.
• 9C. Preda.
  
  Regression models for functional data by reproducing kernel Hilbert spaces methods, in: Journal of Statistical Planning and Inference, 2007, vol. 37, n^o 3, p. 829–840.
• 10C. Preda, G. Saporta, C. Lévéder.
  
  PLS classification for functional data, in: Computational Statistics, 2007, vol. 22, p. 223–235.

Doctoral Dissertations and Habilitation Theses

• 11A. Lourme.
  
  Contribution à la Classification par Modèles de Mélange & Classification Simultanée d’Echantillons d’Origines Multiples, University Lille 1, 2011.

Articles in International Peer-Reviewed Journal

• 12S. Arlot, A. Célisse.
  
  Segmentation in the mean of heteroscedastic data by cross-validation, in: Statistics and Computing, 2011, vol. 21, n^o 4, p. 613–632.
• 13C. Bouveyron, P. Gaubert, J. Jacques.
  
  Adaptive models in regression for modeling and understanding evolving populations, in: Case Studies in Business, Industry and Government Statistics (CSBIGS), 2011, vol. 4, n^o 2.
• 14C. Bouveyron, J. Jacques.
  
  Model-based Clustering of Time Series in Group-specific Functional Subspaces, in: Advances in Data Analysis and Classification, December 2011, vol. 5, n^o 4, p. 281–300.
  
  http://hal.inria.fr/hal-00559561/en
• 15A. Lourme, C. Biernacki.
  
  Simultaneous t-Model-Based Clustering for Data Differing over Time Period: Application for Understanding Companies Financial Health, in: Case Studies in Business, Industry and Government Statistics (CSBIGS), 2011, vol. 4, n^o 2.

Articles in National Peer-Reviewed Journal

• 16A. Célisse, T. Mary-Huard.
  
  Exact Cross-Validation for kNN and applications to passive and active learning in classification, in: Journal de la Société Française de Statistique, 2011.
• 17A. Lourme, C. Biernacki.
  
  Classification simultanée de plusieurs échantillons sous contrainte d’égalité des entropies de partition, in: Journal de la Société Française de Statistique, 2011.

Invited Conferences

• 18A. Aguilera, M. Escabias, C. Preda, G. Saporta.
  
  Functional PLS versus functional PCR through simulated data and chemometric applications, in: 4th international Conference of ERCIM WG on Computing and Statistics (ERCIM'11), 2011.
• 19C. Biernacki, V. Vandewalle.
  
  Label Switching in Mixtures, in: AIP Conference Proceedings, AIP, 2011, vol. 1389, p. 398–401. [ DOI : 10.1063/1.3636747 ]
  
  http://link.aip.org/link/?APC/1389/398/1
• 20C. Preda, A. Amarioarei.
  
  Approximations for the three-dimensional scan statistics, in: International Conference on Advances in Probability and Statistics - Theory and Applications, 2011.
• 21C. Preda.
  
  Functional PLS regression, in: 7th Congress of Romanian Mathematicians, 2011.

International Conferences with Proceedings

• 22C. Bouveyron, J. Jacques.
  
  Model-based Clustering of Time Series in Group-specific Functional Subspaces, in: 12th annual conference of the International Federation of Classification Societies, 2011.
• 23M. Giacofci, S. Lambert-Lacroix, G. Marot, F. Picard.
  
  Wavelet based clustering for mixed-effects functional models, in: International Biometric Society Channel Network conference, 2011.
• 24A. Lourme, C. Biernacki.
  
  Simultaneous $t$-Model-Based Clustering Applied to Company Bankrupt Prediction, in: 8th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, 2011.
• 25C. Preda, J. Schiltz.
  
  Functional PLS regression with functional response, in: Applied Stochastic Models and Data Analysis (ASMDA 2011), 2011, p. 1126-1133.

National Conferences with Proceeding

• 26C. Biernacki, V. Vandewalle.
  
  Label switching dans les mélanges, in: 43e Journées de Statistique, 2011.

Conferences without Proceedings

• 27C. Biernacki, G. Celeux, G. Govaert, F. Langrognet.
  
  Classification des données quantitatives de grande dimension dans l’environnement logiciel MIXMOD, in: 43e Journées de Statistique, 2011.
• 28M.-A. Dillies, G. Marot.
  
  RNA-seq Data Analysis: Lost in Normalization?, in: JOBIM – Journées Ouvertes Biologie Informatique Mathématiques, 2011, with members of the Statomique Consortium.
• 29J. Hamon, C. Dhaenens, J. Jacques, G. Even.
  
  Combining combinatorial optimization and statistics to mine high-throughput genotyping data, in: JOBIM - Journées Ouvertes Biologie Informatique Mathématiques, Paris, France, June 2011.
  
  http://hal.inria.fr/hal-00639533/en
• 30J. Jacques.
  
  Functional PLS regression, in: Astrostatistique en France, 2011.
• 31F. Pierre, A. Reboul, B. Grenier-Boley, G. Marot, M. Guedj, R. Blervaque, D. Hot, C. Pichon, H. Touzet, E. Pradel, F. Sebbane.
  
  Toward the identification and characterization of the Yersinia pestis RNome produced in vivo, in: ASM (American Society for Microbiology) Conference on Regulating with RNA in bacteria, 2011.
• 32V. Vandewalle, C. Biernacki.
  
  Label Switching in Mixtures, in: Working Group on Model-Based Clustering Summer Session, 2011.
• 33L. Yengo, J. Jacques, C. Biernacki.
  
  A Block Regression approach for Simultaneous Variables Clustering and Selection: Application to Genetic Data, in: JOBIM - Journées Ouvertes Biologie Informatique Mathématiques, Paris, France, 2011.

Internal Reports

• 34C. Biernacki, G. Castellan.
  
  A Data-Driven Bound on Variances for Avoiding Degeneracy in Univariate Gaussian Mixtures, Pub. IRMA Lille, 2011, n^o 71-IV.

Other Publications

• 35A. Célisse, J.-J. Daudin, L. Pierre.
  
  Consistency of maximum likelihood and variational estimators in stochastic block model, 2011.
  
  http://arxiv.org/abs/1105.3288
• 36J. Jacques, C. Preda.
  
  Model-based clustering of functional data, 2011.
  
  http://hal.inria.fr/hal-00628247/en
• 37M. Marbac-Lourdelle.
  
  Modèle de mélange pour variables qualitatives reflétant la corrélation entre variables et application à la classification non supervisée, Université Lille 1, 2011.
• 38G. Marot.
  
  Modélisation statistique pour l'analyse de données de puces à ADN, 2011, GEPV laboratory Lille 1 (Génétique et Evolution des Populations Végétales).
• 39G. Marot.
  
  Présentation de Bioconductor et de son utilisation sur les puces à ADN, 2011, séminaire du réseau régional d’ingénieurs en bioinformatique de Lille.
• 40L. Yengo, J. Jacques, C. Biernacki.
  
  A block regression approach for simultaneous clustering and variables selection: application to genetic data, 2011, Seminar Statistics for Systems Biology (SSB).