Bibliography

Major publications by the team in recent years

[1]

C. Amblard, S. Girard.
Estimation procedures for a semiparametric family of bivariate copulas, in: Journal of Computational and Graphical Statistics, 2005, vol. 14, n^o 2, p. 1–15.

[2]

J. Blanchet, F. Forbes.
Triplet Markov fields for the supervised classification of complex structure data, in: IEEE trans. on Pattern Analyis and Machine Intelligence, 2008, vol. 30(6), p. 1055–1067.

[3]

C. Bouveyron, S. Girard, C. Schmid.
High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, p. 502–519.

[4]

C. Bouveyron, S. Girard, C. Schmid.
High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, n^o 14.

[5]

G. Celeux, S. Chrétien, F. Forbes, A. Mkhadri.
A Component-wise EM Algorithm for Mixtures, in: Journal of Computational and Graphical Statistics, 2001, vol. 10, p. 699–712.

[6]

G. Celeux, F. Forbes, N. Peyrard.
EM procedures using mean field-like approximations for Markov model-based image segmentation, in: Pattern Recognition, 2003, vol. 36, n^o 1, p. 131-144.

[7]

F. Forbes, G. Fort.
Combining Monte Carlo and Mean field like methods for inference in hidden Markov Random Fields, in: IEEE trans. PAMI, 2007, vol. 16, n^o 3, p. 824-837.

[8]

F. Forbes, N. Peyrard.
Hidden Markov Random Field Model Selection Criteria based on Mean Field-like Approximations, in: in IEEE trans. PAMI, August 2003, vol. 25(9), p. 1089–1101.

[9]

L. Gardes, S. Girard.
A moving window approach for nonparametric estimation of the conditional tail index, in: Journal of Multivariate Analysis, 2008, vol. 99, p. 2368–2388.

[10]

S. Girard.
A Hill type estimate of the Weibull tail-coefficient, in: Communication in Statistics - Theory and Methods, 2004, vol. 33, n^o 2, p. 205–234.

Publications of the year

Articles in International Peer-Reviewed Journal

[11]

C. Amblard, S. Girard.
A new extension of bivariate FGM copulas, in: Metrika, 2009, vol. 70, p. 1-17
http://hal.inria.fr/inria-00134433/en/.

[12]

C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, S. Girard.
Retrieval of Mars surface physical properties from Omega hyperspectral images using Regularized Sliced Inverse Regression, in: Journal of Geophysical Research - Planets, 2009, vol. 114
http://hal.archives-ouvertes.fr/hal-00383143/en/, E06005.

[13]

C. Bernard-Michel, L. Gardes, S. Girard.
Gaussian Regularized Sliced Inverse Regression, in: Statistics and Computing, 2009, vol. 19, p. 85-98
http://hal.inria.fr/inria-00180458/en/.

[14]

C. Bouveyron, S. Girard.
Robust supervised classification with mixture models: Learning from data with uncertain labels, in: Pattern Recognition, 2009, vol. 42, n^o 11, p. 2649-2658
http://hal.archives-ouvertes.fr/hal-00325263/en/.

[15]

B. Durand, M.-J. Martinez, D. Calavas, C. Ducrot.
Comparison of strategies for substantiating freedom from scrapie in a sheep flock, in: BMC Veterinary Research, 2009, n^o 5:16.

[16]

M. Fauvel, J. Atli Benediktsson, J. Chanussot.
Kernel principal component analysis for the classification of hyperspectral remote-sensing data over urban areas, in: EURASIP Journal on Advances in Signal Processing, 2009, Vol. 2009 p
http://hal.archives-ouvertes.fr/hal-00372296/en/.

[17]

S. Girard, P. Jacob.
Frontier estimation with local polynomials and high power-transformed data, in: Journal of Multivariate Analysis, 2009, vol. 100, p. 1691-1705
http://hal.archives-ouvertes.fr/hal-00384731/en/.

[18]

M.-J. Martinez, C. Lavergne, C. Trottier.
A mixture model-based approach to the clustering of exponential repeated data, in: Journal of Multivariate Analysis, 2009, vol. 100, n^o 9, p. 1938-1951.

[19]

B. Scherrer, M. Dojat, F. Forbes, C. Garbay.
Agentification of Markov model-based segmentation: application to magnetic resonance brain scans., in: Artif Intell Med, 2009, vol. 46, p. 81-95
http://www.hal.inserm.fr/inserm-00381395/en/.

[20]

B. Scherrer, F. Forbes, C. Garbay, M. Dojat.
Distributed Local MRF Models for Tissue and Structure Brain Segmentation., in: IEEE Trans Med Imaging, 2009, vol. 28, p. 1296-1307
http://www.hal.inserm.fr/inserm-00402265/en/.

[21]

M. Vignes, F. Forbes.
Gene clustering via integrated Markov models combining individual and pairwise features, in: IEEE-ACM Trans. Comput. Biol. Bioinform., April 2009, vol. 6, n^o 2, p. 260–270.

Articles in National Peer-Reviewed Journal

[22]

J. Blanchet, F. Forbes, S. Chopart, L. Azizi.
Le logiciel SpaCEM³ pour la classification de données complexes, in: Rev. Modulad, June 2009, vol. 40, p. 147–166.

[23]

C. Bouveyron, S. Girard.
Classification supervisée et non supervisée des données de grande dimension, in: La Revue de Modulad, 2009, vol. 40, p. 81-102
http://hal.archives-ouvertes.fr/hal-00394327/en/.

International Peer-Reviewed Conference/Proceedings

[24]

C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, S. Girard.
Machine learning techniques for the inversion of planetary hyperspectrals images, in: 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, august 2009.

[25]

C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, S. Girard.
Support vectors machines regression for estimation of Mars surface physical properties, in: 17th European Symposium on Artificial Neural Networks, Bruges, Belgique, april 2009, p. 195–200.

[26]

C. Bouveyron, S. Girard, M. Olteanu.
Supervised classification of categorical data with uncertain labels for DNA barcoding, in: 17th European Symposium on Artificial Neural Networks, Belgique Bruges, april 2009, p. 29-34
http://hal.archives-ouvertes.fr/hal-00407834/en/.

[27]

A. Daouia, L. Gardes, S. Girard, A. Lekina.
Extreme level curves of heavy-tailed distributions, in: 6th International Conference on Extreme Value Analysis, Fort Collins, USA, june 2009.

[28]

M. Fauvel, J. Chanussot, J. Atli Benediktsson.
Kernel Principal Component Analysis for the construction of the extended morphological profile, in: Proceedings of the IEEE International Geoscience And Remote Sensing Symposium 2009, IGARSS 2009 IEEE International Geoscience And Remote Sensing Symposium 2009, IGARSS 2009, South Africa, 2009, 2009 p
http://hal.archives-ouvertes.fr/hal-00372304/en/.

[29]

F. Forbes, W. Pieczynski.
New trends in Markov models and related learning to restore data, in: IEEE Conference on Machine Learning and Signal Processing, MLSP 09, September, 2009, Grenoble, France, 2009.

[30]

L. Gardes, S. Girard, A. Guillou.
A unified statistical model for Pareto and Weibull tail distributions, in: 6th International Conference on Extreme Value Analysis, Fort Collins, USA, june 2009.

[31]

P. Loiseau, P. Gonçalves, S. Girard, F. Forbes, P. Primet Vicat-Blanc.
Maximum likelihood estimation of the flow size distribution tail index from sampled packet data, in: SIGMETRICS–Joint International Conference on Measurement and Modeling of Computer Systems, Seattle, USA, june 2009.

[32]

B. Scherrer, F. Forbes, M. Dojat.
A conditional random field approach for coupling local registration with robust tissue and structure segmentation, in: 12th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2009, September, 2009, Londres, Royaume-Uni, 2009.

Workshops without Proceedings

[33]

C. Bernard-Michel, L. Gardes, S. Girard, G. Molinié.
Spatial analysis of extreme rainfalls in the Cévennes-Vivarais region, in: Spatial Extremes, Theory and Applications, Lisbonne, Portugal, april 2009.

[34]

L. Gardes, S. Girard, A. Lekina.
Estimation non-paramétrique des quantiles extrêmes conditionnels, in: 41èmes Journées de Statistique, SFdS, Bordeaux, France Bordeaux, France, may 2009
http://hal.inria.fr/inria-00386572/en/.

[35]

J. Hinde, S. de Freitas, M.-J. Martinez, C. Demetrio.
Random effects in cumulative mortality models, in: 2nd Workshop of the ERCIM Working Group on Computing and Statistics, Limassol,Cyprus, october 2009.

Scientific Books (or Scientific Book chapters)

[36]

B. Waske, M. Fauvel, J. Chanussot, J. Atli Benediktsson.
Machine learning techniques in remote sensing data analysis, in: Kernel methods for Remote Sensing Data Analysis, Edited by Gustavo Camps-Valls and Lorenzo Bruzzone (John Wiley and Sons), 2009.

Internal Reports

[37]

V. Ciriza, L. Donini, J.-B. Durand, S. Girard.
User-friendly power management algorithms, Xerox/INRIA, 2009
http://hal.archives-ouvertes.fr/hal-00412509/en/, Technical report.

[38]

A. Daouia, L. Gardes, S. Girard.
Large sample approximation of the distribution for smooth monotone frontier estimation, Xerox/INRIA, 2009
http://hal.archives-ouvertes.fr/hal-00409447/en/, Technical report.

[39]

A. Daouia, L. Gardes, S. Girard, A. Lekina.
Kernel estimators of extreme level curves, GREMAQ/INRIA, 2009
http://hal.inria.fr/inria-00393588/en/, Technical report.

[40]

L. Gardes, S. Girard.
Conditional extremes from heavy-tailed distributions: An application to the estimation of extreme rainfall return levels, LJK/INRIA, 2009
http://hal.archives-ouvertes.fr/hal-00371757/en/, Technical report.

[41]

R. P. Horaud, F. Forbes, M. Yguel, G. Dewaele, J. Zhang.
Rigid and Articulated Point Registration with Expectation Conditional Maximization, INRIA, November 2009, n^o RR-7114, Rapport de recherche.

[42]

V. Khalidov, F. Forbes, R. P. Horaud.
Conjugate Mixture Models for Clustering Multimodal Data, INRIA, November 2009, n^o RR-7117, Rapport de recherche.

[43]

R. Narasimha, É. Arnaud, F. Forbes, R. P. Horaud.
A Joint Framework for Disparity and Surface Normal Estimation, INRIA, October 2009, n^o RR-7090, Rapport de recherche.

References in notes

[44]

J. Ashburner, K. J. Friston.
Unified Segmentation, in: NeuroImage, 2005, vol. 26, p. 839–851.

[45]

C. Biernacki, G. Celeux, G. Govaert, F. Langrognet.
Model-Based Cluster and Discriminant Analysis with the MIXMOD Software, in: Computational Statistics and Data Analysis, 2006, vol. 51, n^o 2, p. 587–600.

[46]

C. Bouveyron.
Modélisation et classification des données de grande dimension. Application à l'analyse d'images, Université Grenoble 1, septembre 2006
http://tel.archives-ouvertes.fr/tel-00109047, Ph. D. Thesis.

[47]

C. Chen, F. Forbes, O. Francois.
FASTRUCT: Model-based clustering made faster, in: Molecular Ecology Notes, 2006, vol. 6, p. 980–983.

[48]

V. Ciriza, L. Donini, J.-B. Durand, S. Girard.
User-friendly power management algorithms, 2009
http://hal.archives-ouvertes.fr/hal-00412509/fr/.

[49]

A. Daouia, L. Gardes, S. Girard, A. Lekina.
Kernel estimators of extreme level curves, 2009
http://hal.inria.fr/inria-00393588/fr/.

[50]

P. Embrechts, C. Klüppelberg, T. Mikosh.
Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.

[51]

F. Ferraty, P. Vieu.
Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.

[52]

O. Francois, S. Ancelet, G. Guillot.
Bayesian clustering using Hidden Markov Random Fields in spatial genetics, in: Genetics, 2006, p. 805–816.

[53]

L. Gardes.
Estimation d'une fonction quantile extrême, Université Montpellier 2, october 2003, Ph. D. Thesis.

[54]

L. Gardes, S. Girard.
Conditional extremes from heavy-tailed distributions: An application to the estimation of extreme rainfall return levels, LJK/INRIA, 2009
http://hal.archives-ouvertes.fr/hal-00371757/fr/, Technical report.

[55]

L. Gardes, S. Girard, A. Guillou.
Weibull tail-distributions revisited: a new look at some tail estimators, 2009
http://hal.archives-ouvertes.fr/hal-00340661/fr/.

[56]

L. Gardes, S. Girard, A. Lekina.
Functional nonparametric estimation of conditional extreme quantiles, in: Journal of Multivariate Analysis, 2010, vol. 101, p. 419–433.

[57]

M. Garrido.
Modélisation des événements rares et estimation des quantiles extrêmes, méthodes de sélection de modèles pour les queues de distribution, Université Grenoble 1, juin 2002
http://mistis.inrialpes.fr/people/girard/Fichiers/theseGarrido.pdf, Ph. D. Thesis.

[58]

S. Girard.
Construction et apprentissage statistique de modèles auto-associatifs non-linéaires. Application à l'identification d'objets déformables en radiographie. Modélisation et classification, Université de Cery-Pontoise, octobre 1996, Ph. D. Thesis.

[59]

K. Li.
Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, p. 316–327.

[60]

S. Makni, J. Idier, T. Vincent, B. Thirion, G. Dehaene-Lambertz, P. Ciuciu.
A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI., in: NeuroImage, 07 2008, vol. 41, n^o 3, p. 941-69
http://hal-cea.archives-ouvertes.fr/cea-00333624/en/.

[61]

R. Nelsen.
An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.

[62]

K. Pohl, J. Fisher, E. Grimson, R. Kikinis, W. Wells.
A Bayesian model for joint segmentation and registration, in: NeuroImage, 2006, vol. 31, n^o 1, p. 228-239.

[63]

J. Pritchard, M. Stephens, P. Donnelly.
Inference of Population Structure Using Multilocus Genotype Data, in: Genetics, 2000, vol. 155, p. 945–959.

[64]

B. Scherrer, F. Forbes, M. Dojat, C. Garbay.
Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation. Received the Young Investigator Award in Segmentation, in: MICCAI 2008, New-York, USA, 2008, p. 1066-74.

[65]

M. Seghier, A. Ramlackhansingh, J. Crinion, A. Leff, C. J. Price.
Lesion identification using unified segmentation-normalisation models and fuzzy clustering, in: Neuroimage, 2008, vol. 41, p. 1253-1266.

[66]

K. Van Leemput, F. Maes, D. Vandermeulen, A. Colchester, P. Suetens.
Automated segmentation of Multiple Sclerosis Lesions by model outlier detection, in: IEEE trans. Med. Ima., 2001, vol. 20, n^o 8, p. 677-688.