Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]

J.-P. Baudry.
Sélection de modèle pour la classification non supervisée. Choix du nombre de classes., Université Paris-Sud, 2009, Ph. D. Thesis.

[2]

V. Vandewalle.
Estimation et sélection de modèle en classification semi-supervisée., Université Lille 1, 2009
http://tel.archives-ouvertes.fr/tel-00447141/PDF/These.pdf, Ph. D. Thesis.

Articles in International Peer-Reviewed Journal

[3]

A. Antoniadis, I. Gijbels, J.-M. Poggi.
Smoothing non equispaced heavy noisy data with wavelets, in: Statistica Sinica, 2009, vol. 19, n^o 4, p. 1371–1387.

[4]

S. Arlot.
Model selection by resampling penalization, in: Electron. J. Statist., 2009, vol. 3, p. 557–624 (electronic).

[5]

S. Arlot, G. Blanchard, É. Roquain.
Some non-asymptotic results on resampling in high dimension, I: confidence regions, in: Annals of Statistics, 2009, To appear.

[6]

S. Arlot, G. Blanchard, É. Roquain.
Some non-asymptotic results on resampling in high dimension, II: multiple tests, in: Annals of Statistics, 2009, To appear.

[7]

S. Arlot, P. Massart.
Data-driven calibration of penalties for least-squares regression, in: J. Mach. Learn. Res., 2009, vol. 10, p. 245–279 (electronic)
http://hal.archives-ouvertes.fr/hal-00243116/PDF/arlot08a.pdf.

[8]

S. Boucheron, G. Lugosi, P. Massart.
On concentration of self-bounding functions, in: Electronic Journal of Probability, 2009, vol. 14, p. 1884-1899.

[9]

S. Boucheron, P. Massart.
A high dimensional Wilks phenomenon, in: Probability Theory and Related Fields, 2009, submitted.

[10]

G. Celeux, A. Grimaud, Y. Lefebvre, E. De Rocquigny.
Identifying variability in multivariate systems through linearised inverse methods, in: Inverse Problems in Engineering, 2009, to appear.

[11]

L. Cucala, J.-M. Marin, C. Robert, M. Titteringtion.
A Bayesian reassessment of nearest-neighbour classification, in: J. Amer. Statist. Assoc., 2009, vol. 104, n^o 485, p. 263–273.

[12]

E. Eger, V. Michel, B. Thirion, A. Amadon, S. Dehaene, A. Kleinschmidt.
Deciphering Cortical Number Coding from Human Brain Activity Patterns, in: Current Biology, 2009, vol. 19, p. 1608-1615.

[13]

A. Iacobucci, J.-M. Marin, C. Robert.
On variance stabilisation by double Rao-Blackwellisation, in: Comput. Statist. Data Anal., 2010, (to appear).

[14]

F.-X. Jollois, J.-M. Poggi, B. Portier.
Three non-linear statistical methods to analyze PM10 pollution in Rouen area, in: CS-BIGS, 2009, vol. 3, n^o 1, p. 1–17.

[15]

A. Knops, B. Thirion, E. Hubbard, V. Michel, S. Dehaene.
Mathematics as cortical recycling : Recruitment of an area involved in eye movements during mental arithmetic, in: Science, 2009, vol. 324, p. 1583-1585.

[16]

M. Lebreton, S. Jorge, V. Michel, B. Thirion, M. Pessiglione.
An automatic valuation system in the human brain : evidence from functional neuroimaging, in: Neuron, 2009, (to appear).

[17]

C. Maugis, G. Celeux, M.-L. Martin-Magniette.
Variable selection for Clustering with Gaussian Mixture Models, in: Biometrics, 2009, vol. 65, p. 701-709.

[18]

C. Maugis, G. Celeux, M.-L. Martin-Magniette.
Variable selection in model-based clustering: A general variable role modeling, in: Computational Statistics and Data Analysis, 2009, vol. 53, p. 3872-3882.

[19]

C. Maugis, B. Michel.
A non asymptotic penalized criterion for Gaussian mixture model selection, in: ESAIM, P & S, 2009, To appear.

[20]

J. Perry, C. ter Braak, P. Dixon, J. Duan, R. Hails, A. Huesken, M. Lavielle, M. Marvier, M. Scardi, K. Schmidt, B. Tothmeresz, F. Schaarschmidt, H. van der Voet.
Statistical aspects of environmental risk assessment of GM plants for effects on non-target organisms, in: Environmental Biosafety Research, 2009, vol. 8, p. 65–78.

[21]

R. Savic, M. Lavielle.
A new SAEM algorithm: Performance in Population Models for Count Data, in: Journal of Pharmacokinetics and Pharmacodynamics, 2009, vol. 36, p. 367–379.

[22]

N. Verzelen.
Adaptive estimation of Stationary Gaussian fields, in: Annals of Statistics, 2009
http://hal.inria.fr/inria-00353251/PDF/RR-6797.pdf, to appear.

[23]

N. Verzelen.
High-dimensional Gaussian model selection on a Gaussian design, in: Ann. Inst. H. Poincaré Probab. Statist., 2009, to appear.

[24]

N. Verzelen, F. Villers.
Goodness-of-fit Tests for high-dimensional Gaussian linear models, in: Annals of Statistics, 2009, to appear.

[25]

N. Verzelen, F. Villers.
Tests for Gaussian graphical models, in: Comput. Statist. Data Analysis, 2009, vol. 53, p. 1894–1905.

Articles in National Peer-Reviewed Journal

[26]

C. Maugis, M.-L. Martin-Magniette, J.-P. Tamby, J.-P. Renou, A. Lecharny, S. Aubourg, G. Celeux.
Sélection de variables pour la classification par mélanges gaussiens pour prédire la fonction des gènes orphelins, in: MODULAD, 2009, vol. 40, p. 69-80.

Invited Conferences

[27]

G. Celeux.
Sélection de modèle pour la classification en présence d'une classification externe, in: Journées de Probabilité 2009, Poitiers, June 2009.

[28]

M. Delattre, M. Lavielle.
Estimation of Mixed Hidden Markov Models with SAEM. Application to daily seizures data, in: PKUK Meeting, Birmingham, UK, November 2009.

[29]

M. Delattre, M. Lavielle.
Estimation of Mixed Hidden Markov Models with SAEM. Application to daily seizures data, in: ACOP Meeting, Mystic, US, October 2009.

[30]

R. Genuer, J.-M. Poggi, C. Tuleau.
Random Forests: variable importance and variable selection, in: SFC'2009, Grenoble, September 2009.

[31]

R. Genuer, J.-M. Poggi, C. Tuleau.
Random Forests: variable importance and variable selection, in: Colloquium Statistiques pour le traitement de l'image, Paris, January 2009.

[32]

R. Genuer, J.-M. Poggi, C. Tuleau.
Variable Selection using Random Forests, in: International Meeting on Statistical Methods for the Analysis of Large Data-Sets, Pescara, September 2009.

[33]

F.-X. Jollois, J.-M. Poggi, B. Portier.
Three non-linear statistical methods to analyze PM10 pollution in Rouen area, in: TIES 2009 - the 20th Annual Conference of The International Environmetrics Society and GRASPA Conference 2009, Bologna, July 2009.

[34]

M. Lavielle.
Analysing Population PK-PD Data with the SAEM Algorithm and MONOLIX, in: Population PK-PD Meeting, London, UK, October 2009.

[35]

M. Lavielle.
Problèmes statistiques de l'interprétation des essais toxicologiques, in: Colloque "Les OGM face aux nouveaux paradigmes de la biologie", Paris, February 2009.

[36]

M. Lavielle.
Some comments about the statistical methodology used for the analysis of toxicity tests, in: Biosafenet Meeting, Ca Tron di Roncade, italy, January 2009.

[37]

M. Lavielle, F. Mentré.
Estimation et planification dans les modèles non linéaires à effets mixtes. Application à la dynamique du VIH sous traitement, in: 41èmes Journées de Statistique, Bordeaux, May 2009
http://hal.inria.fr/inria-00386791/PDF/p234.pdf.

[38]

M. Lavielle, A. Samson, A. K. Firmin, F. Mentré.
Parameter estimation of long-term HIV dynamic model in the COPHAR2 - ANRS 111 trial using MONOLIX, in: Joint Statistical Meeting, Washington, US, August 2009.

[39]

M. Lavielle, R. Savic.
Modeling odd-type data with MONOLIX, in: POPSIM Meeting, Copenhagen, Danmark, September 2009.

[40]

N. Verzelen.
Data-driven neighborhood selection of a Gaussien field, in: The 20th Annual Conference of The International Environmetrics Society, Bologna, Italy, July 2009.

International Peer-Reviewed Conference/Proceedings

[41]

R. Genuer, V. Michel, E. Eger, B. Thirion.
Random Forests based feature selection for decoding fMRI data, in: submitted to IEEE International Symposium on Biomedical Imaging, November 2009.

[42]

M. Keller, M. Lavielle, M. Perrot, A. Roche.
Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis, in: 12th MICCAI, London, U.K., September 2009.

[43]

C. Maugis.
Variable selection in model-based clustering: A general variable role modeling, in: Classification and Data Analysis (CLADAG), Catania, September 2009.

[44]

V. Michel, E. Eger, C. Keribin, B. Thirion.
Adaptive multi-class Bayesian sparse regression - An application to brain activity classification, in: MICCAI'09 Workshop on Analysis of Functional Medical Images, 2009.

National Peer-Reviewed Conference/Proceedings

[45]

V. Vandewalle.
A data-driven penalized criterion for Gaussian mixture model selection, in: 41èmes Journées de Statistique, Bordeaux, May 2009.

Workshops without Proceedings

[46]

J.-P. Baudry.
Critères de sélection de modèles consistants pour la classification non supervisée., in: Séminaire MAP5, Paris, October 2009.

[47]

J.-P. Baudry.
Sélection de modèle pour la classification non supervisée., in: Séminaire SAMOS-Paris I, Paris, October 2009.

[48]

J.-P. Baudry, G. Celeux.
Sélection de modèle pour la classification en présence d'une classification externe, in: 41èmes Journées de Statistique, Bordeaux, May 2009
http://hal.inria.fr/inria-00386620/PDF/p66.pdf.

[49]

J.-P. Baudry, C. Maugis, B. Michel.
How to put the slope heuristics in practice, in: Summer Working Group on Model-Based Clustering, Paris, July 2009, Poster.

[50]

G. Celeux.
Clustering Model Selection related to an External Classification, in: Summer Working Group on Model-Based Clustering, Paris, July 2009.

[51]

M. Delattre.
Application des modèles de Markov cachés à effets mixtes à des données d'épilepsie, in: Journées du GDR Statistique et Santé, Paris, October 2009.

[52]

M. Delattre, M. Lavielle.
Estimation of Mixed Hidden Markov Models with SAEM. Application to daily seizures data, in: CLAPEM, Naiguata, Venezuela, November 2009.

[53]

M. El Anbari, A. Mkhadri.
Regularization and variable selection via the Larcop, in: 41èmes Journées de Statistique, Bordeaux, May 2009.

[54]

G. Govaert, G. Celeux.
Block clustering and mixture models, in: Summer Working Group on Model-Based Clustering, Paris, July 2009.

[55]

C. Maugis, G. Celeux, M.-L. Martin-Magniette.
Sélection de variables pour la classification non supervisée par mélanges gaussiens et pour l'analyse discriminante gaussienne, in: 41èmes Journées de Statistique, Bordeaux, May 2009.

[56]

C. Maugis.
Variable selection for model-based clustering and discriminant analysis, in: Summer Working Group on Model-Based Clustering, Paris, July 2009.

[57]

A. Pasanisi, C. Roero, G. Celeux, E. Rémy.
Quelques considérations sur l'utilisation pratique des modèles discrets de survie en fiabilité industrielle, in: 41èmes Journées de Statistique, Bordeaux, May 2009
http://hal.inria.fr/inria-00386574/PDF/p20.pdf.

[58]

V. Vandewalle.
Model selection in semi-supervised classification, in: Working Group on Model-Based Clustering Summer Session, Paris, July 2009.

[59]

V. Vandewalle.
Sélection de modèles en classification semi-supervisée, in: Groupe de travail AgroParisTech-Paris Descartes-select, Paris, March 2009.

Scientific Books (or Scientific Book chapters)

[60]

G. Celeux.
Discriminant Analysis, in: Data Analysis, John Wiley, 2009, p. 181-214.

Internal Reports

[61]

Y. Auffray, P. Barbillon.
Conditionally positive definite kernels : theoretical contribution, application to interpolation and approximation, Institut National de Recherche en Informatique et Automatique, 2009, n^o RR-6835
http://hal.inria.fr/inria-00359944/PDF/RR-6835.pdf, Technical report.

[62]

Y. Auffray, P. Barbillon, J.-M. Marin.
Maximin Design on non-hypercube domain and Kernel Interpolation, Institut National de Recherche en Informatique et Automatique, 2009, Technical report.

[63]

P. Barbillon, G. Celeux, A. Grimaud, Y. Lefebvre, E. De Rocquigny.
Non linear methods for inverse statistical problems, Institut National de Recherche en Informatique et Automatique, 2009
http://hal.inria.fr/inria-00441967/PDF/RR-7156.pdf, Technical report.

[64]

J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo, R. Gottardo.
Combining Mixture Components for Clustering, Institut National de Recherche en Informatique et Automatique, 2009, n^o 6644, Research Report.

[65]

C. Giraud, S. Huet, N. Verzelen.
Graph selection with GGMselect, hal-00401550, 2009
http://hal.archives-ouvertes.fr/hal-00401550/en/, Technical report.

[66]

C. Keribin.
Les méthodes bayésiennes variationnelles et leur application en neuroimagerie: une étude de l'existant, Institut National de Recherche en Informatique et Automatique, 2009, n^o RR-7091
http://hal.inria.fr/inria-00430289/PDF/RR-7091.pdf, Technical report.

[67]

C. Maugis, B. Michel.
Slope heuristics for variable selection and clustering via Gaussian mixtures, Institut National de Recherche en Informatique et Automatique, 2009, n^o RR-6550
http://hal.inria.fr/docs/00/28/50/32/PDF/RR-6550.pdf, Technical report.

[68]

M. Misiti, Y. Misiti, G. Oppenheim, J.-M. Poggi.
Stratégie divisive pour la prévision par désagrégation - Parallélisation et effet de la taille des données, Rapport EDF (60 pages), 2009, Technical report.

[69]

N. Verzelen.
Data-driven neighborhood selection of a Gaussian field, Institut National de Recherche en Informatique et Automatique, 2009, n^o 6798
http://hal.inria.fr/inria-00353260/PDF/RR-6798.pdf, Technical report.

Scientific Popularization

[70]

V. Vandewalle.
Les modèles de mélange, un outil utile pour la classification semi-supervisée, in: La revue MODULAD, 2009, vol. 40, p. 121-145.