Members
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
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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, no 2, pp. 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), pp. 1055–1067.
[3]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, pp. 502–519.
[4]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, no 14.
[5]
L. Chaari, T. Vincent, F. Forbes, M. Dojat, P. Ciuciu.
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach, in: IEEE Transactions on Medical Imaging, May 2013, vol. 32, no 5, pp. 821-837. [ DOI : 10.1109/TMI.2012.2225636 ]
http://hal.inria.fr/inserm-00753873
[6]
A. Deleforge, F. Forbes, R. Horaud.
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables, in: Statistics and Computing, February 2014. [ DOI : 10.1007/s11222-014-9461-5 ]
https://hal.inria.fr/hal-00863468
[7]
F. Forbes, G. Fort.
Combining Monte Carlo and Mean field like methods for inference in hidden Markov Random Fields, in: IEEE trans. Image Processing, 2007, vol. 16, no 3, pp. 824-837.
[8]
F. Forbes, D. Wraith.
A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweights: Application to robust clustering, in: Statistics and Computing, November 2014, vol. 24, no 6, pp. 971-984. [ DOI : 10.1007/s11222-013-9414-4 ]
https://hal.inria.fr/hal-00823451
[9]
S. Girard.
A Hill type estimate of the Weibull tail-coefficient, in: Communication in Statistics - Theory and Methods, 2004, vol. 33, no 2, pp. 205–234.
[10]
S. Girard, P. Jacob.
Extreme values and Haar series estimates of point process boundaries, in: Scandinavian Journal of Statistics, 2003, vol. 30, no 2, pp. 369–384.
Publications of the year

Articles in International Peer-Reviewed Journals

[11]
M. Albughdadi, L. Chaari, J.-Y. Tourneret, F. Forbes, P. Ciuciu.
A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation, in: Signal Processing, 2017.
https://hal.archives-ouvertes.fr/hal-01426385
[12]
M.-R. Amini, J.-B. Durand, O. Gaudoin, E. Gaussier, A. Iouditski.
Data Science: an international training program at master level, in: Statistique et Enseignement (ISSN 2108-6745), June 2016, vol. 7, no 1, pp. 95-102.
https://hal.inria.fr/hal-01342469
[13]
J. Arbel, V. Costemalle.
Estimation of immigration flows : reconciling two sources by a Bayesian approach, in: Economie et Statistique, April 2016.
https://hal.archives-ouvertes.fr/hal-01396606
[14]
A. Chiancone, F. Forbes, S. Girard.
Student Sliced Inverse Regression, in: Computational Statistics and Data Analysis, August 2016. [ DOI : 10.1016/j.csda.2016.08.004 ]
https://hal.archives-ouvertes.fr/hal-01294982
[15]
A. Chiancone, S. Girard, J. Chanussot.
Collaborative Sliced Inverse Regression, in: Communication in Statistics - Theory and Methods, 2016, forthcoming. [ DOI : 10.1080/03610926.2015.1116578 ]
https://hal.inria.fr/hal-01158061
[16]
J.-B. Durand, Y. Guédon.
Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models, in: Statistics and Computing, 2016, vol. 26, no 1, pp. 549-567, The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-014-9494-9. [ DOI : 10.1007/s11222-014-9494-9 ]
https://hal.inria.fr/hal-01090836
[17]
F. Durante, S. Girard, G. Mazo.
Marshall–Olkin type copulas generated by a global shock, in: Journal of Computational and Applied Mathematics, April 2016, vol. 296, pp. 638–648. [ DOI : 10.1016/j.cam.2015.10.022 ]
https://hal.archives-ouvertes.fr/hal-01138228
[18]
L. Gardes, S. Girard.
On the estimation of the functional Weibull tail-coefficient, in: Journal of Multivariate Analysis, 2016, vol. 146, pp. 29–45.
https://hal.archives-ouvertes.fr/hal-01063569
[19]
I. D. Gebru, X. Alameda-Pineda, F. Forbes, R. Horaud.
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2016, vol. 38, no 12, pp. 2402 - 2415. [ DOI : 10.1109/TPAMI.2016.2522425 ]
https://hal.inria.fr/hal-01261374
[20]
S. Girard, G. Stupfler.
Intriguing properties of extreme geometric quantiles, in: REVSTAT - Statistical Journal, 2016, forthcoming.
https://hal.inria.fr/hal-00865767
[21]
P. Jordanova, Z. Fabián, P. Hermann, L. Strelec, A. Rivera, S. Girard, S. Torres, M. Stehlík.
Weak properties and robustness of t-Hill estimators, in: Extremes, 2016, vol. 19, no 4, pp. 591–626.
https://hal.archives-ouvertes.fr/hal-01327002
[22]
G. Mazo, S. Girard, F. Forbes.
A flexible and tractable class of one-factor copulas, in: Statistics and Computing, September 2016, vol. 26, no 5, pp. 965-979.
https://hal.archives-ouvertes.fr/hal-00979147
[23]
P. Mesejo, O. Ibáñez, O. Cordón, S. Cagnoni.
A Survey on Image Segmentation using Metaheuristic-based Deformable Models: State of the Art and Critical Analysis, in: Applied Soft Computing, April 2016.
https://hal.archives-ouvertes.fr/hal-01282678
[24]
P. Mesejo, D. Pizarro, A. Abergel, O. Rouquette, S. Beorchia, L. Poincloux, A. Bartoli.
Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy, in: IEEE Transactions on Medical Imaging, 2016. [ DOI : 10.1109/TMI.2016.2547947 ]
https://hal.archives-ouvertes.fr/hal-01291797
[25]
P. Mesejo, S. Saillet, O. David, C. Bénar, J. M. Warnking, F. Forbes.
A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data, in: IEEE Journal of Selected Topics in Signal Processing, March 2016, vol. 10, no 2, pp. 416-427. [ DOI : 10.1109/JSTSP.2015.2502553 ]
https://hal.inria.fr/hal-01221115
[26]
M. Stehlik, P. Aguirre, S. Girard, P. Jordanova, J. Kiseľák, S. Torres-Leiva, Z. Sadovsky, A. Rivera.
On ecosystems dynamics, in: Ecological Complexity, 2016, forthcoming.
https://hal.inria.fr/hal-01394734
[27]
W. Yang, B. Pallas, J.-B. Durand, S. S. Martinez, M. Han, E. Costes.
The impact of long-term water stress on tree architecture and production is related to changes in transitions between vegetative and reproductive growth in the ‘Granny Smith’ apple cultivar, in: Tree Physiology, September 2016. [ DOI : 10.1093/treephys/tpw068 ]
https://hal.inria.fr/hal-01377095

Invited Conferences

[28]
A. Daouia, S. Girard, G. Stupfler.
Estimation of the marginal expected shortfall using extreme expectiles, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01415581
[29]
A. Daouia, S. Girard, G. Stupfler.
Tail risk estimation based on extreme Lp-quantiles, in: Workshop "Extreme value modeling and water ressources", Aussois, France, 2016.
https://hal.archives-ouvertes.fr/hal-01340767
[30]
A. Deleforge, F. Forbes.
Rectified binaural ratio: A complex T-distributed feature for robust sound localization, in: European Signal Processing Conference, Budapest, Hungary, August 2016, pp. 1257-1261.
https://hal.inria.fr/hal-01372337
[31]
J. El Methni, L. Gardes, S. Girard.
Estimation of risk measures for extreme pluviometrical measurements, in: Workshop "Extreme value modeling and water ressources", Aussois, France, 2016.
https://hal.archives-ouvertes.fr/hal-01340774
[32]
J. El Methni, L. Gardes, S. Girard.
Estimation of risk measures for extreme pluviometrical measurements, in: 26th Annual Conference of The International Environmetrics Society, Edimbourg, United Kingdom, July 2016.
https://hal.archives-ouvertes.fr/hal-01350104
[33]
J. El Methni, L. Gardes, S. Girard.
Frontier estimation based on extreme risk measures, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01415591
[34]
J. El Methni, S. Girard, L. Gardes.
Kernel estimation of extreme risk measures for all domains of attraction, in: Extremes, Copulas and Actuarial Sciences, Marseille, France, February 2016.
https://hal.inria.fr/hal-01312846
[35]
F. Forbes, A. Chiancone, S. Girard.
Student sliced inverse regression, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01415576
[36]
F. Forbes, A. Chiancone, S. Girard.
Student Sliced Inverse Regression, in: 23th summer session of the Working Group on Model-based Clustering, Paris, France, July 2016.
https://hal.archives-ouvertes.fr/hal-01423626
[37]
L. Gardes, S. Girard.
Estimation of the functional Weibull-tail coefficient, in: 3rd conference of the International Society for Non-Parametric Statistics (ISNPS), Avignon, France, June 2016.
https://hal.inria.fr/hal-01366174
[38]
S. Girard, C. Albert, A. Dutfoy.
Extrapolation dans les queues de distribution avec la théorie des valeurs extrêmes, in: Journée estimation de probabilités d'événements rares en maitrise des risques et en sûreté de fonctionnement, Cachan, France, Institut de Maitrise des Risques (IMdR), 2016.
https://hal.archives-ouvertes.fr/hal-01330131
[39]
S. Girard, A. Daouia, G. Stupfler.
Estimation of extreme expectiles from heavy tailed distributions, in: 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01415586
[40]
S. Girard, A. Daouia, G. Stupfler.
Estimation of tail risk based on extreme expectiles, in: Workshop Extremes - Copulas - Actuarial science, Luminy, France, February 2016.
https://hal.archives-ouvertes.fr/hal-01311778
[41]
S. Girard, A. Daouia, G. Stupfler.
Tail risk estimation based on extreme Lp-quantiles, in: Statistics workshop Tilburg University, Tilburg, Netherlands, December 2016.
https://hal.archives-ouvertes.fr/hal-01415533

International Conferences with Proceedings

[42]
M. Albughdadi, L. Chaari, F. Forbes, J.-Y. Tourneret, P. Ciuciu.
Multi-subject joint parcellation detection estimation in functional MRI, in: 13th IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic, April 2016.
https://hal.inria.fr/hal-01261982
[43]
P. Fernique, A. DAMBREVILLE, J.-B. Durand, C. Pradal, P.-E. P.-E. Lauri, F. Normand, Y. Guédon.
Characterization of mango tree patchiness using a tree-segmentation/clusteringapproach, in: 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA 2016), Qingdao, China, November 2016.
https://hal.inria.fr/hal-01398291
[44]
B. Pallas, W. Yang, J.-B. Durand, S. S. Martinez, E. E. Costes.
Impact of Long Term Water Deficit on Production and Flowering Occurrence in the 'Granny Smith' Apple Tree Cultivar, in: XI International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, Bologna, Italy, XI International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, Prof. Dr. Luca Corelli-Grappadelli, Department of Agricultural Sciences, Università di Bologna, August 2016.
https://hal.inria.fr/hal-01377104

National Conferences with Proceedings

[45]
J.-B. Durand, A. Guérin-Dugué, S. Achard.
Analyse de séquences oculométriques et d'électroencéphalogrammes par modèles markoviens cachés, in: 48èmes Journées de Statistique, Montpellier, France, May 2016.
https://hal.inria.fr/hal-01339458

Conferences without Proceedings

[46]
C. Albert, A. Dutfoy, S. Girard.
Encadrement de l'erreur asymptotique d'estimation des quantiles extrêmes, in: 48èmes Journées de Statistique organisées par la Société Française de Statistique, Montpellier, France, May 2016.
https://hal.archives-ouvertes.fr/hal-01326839
[47]
J. Arbel, I. Prünster.
Truncation error of a superposed gamma process in a decreasing order representation, in: NIPS - 30th Conference on Neural Information Processing Systems, Barcelone, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01405580
[48]
J. Arbel, J.-B. Salomond.
Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models, in: NIPS - Conference on Neural Information Processing Systems, Barcelone, Spain, December 2016.
https://hal.archives-ouvertes.fr/hal-01405568
[49]
G. Kon Kam King, J. Arbel, I. Prünster.
A Bayesian nonparametric approach to ecological risk assessment, in: 3rd Bayesian Young Statisticians Meeting (BAYSM 2016), Florence, Italy, June 2016.
https://hal.archives-ouvertes.fr/hal-01405593
[50]
M. Lopes, M. Fauvel, S. Girard, D. Sheeren.
High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series, in: IGARSS 2016 - IEEE International Geoscience and Remote Sensing Symposium, Bejing, China, July 2016.
https://hal.archives-ouvertes.fr/hal-01366208
[51]
M. Lopes, S. Girard, M. Fauvel.
Divergence de Kullback-Leibler en grande dimension pour la classification des prairies à partir de séries temporelles d'images satellite à haute résolution, in: 48èmes Journées de Statistique organisées par la Société Française de Statistique, Montpellier, France, May 2016.
https://hal.archives-ouvertes.fr/hal-01326836
[52]
E. Perthame, F. Forbes, B. Olivier, A. Deleforge.
Non linear robust regression in high dimension, in: The XXVIIIth International Biometric Conference, Victoria, Canada, July 2016.
https://hal.archives-ouvertes.fr/hal-01423622
[53]
E. Perthame, F. Forbes, B. Olivier, A. Deleforge.
Regression non lineaire robuste en grande dimension, in: 48èmes Journées de Statistique organisées par la Société Française de Statistique, Montpellier, France, May 2016.
https://hal.archives-ouvertes.fr/hal-01423630

Scientific Books (or Scientific Book chapters)

[54]
F.-B. Didier, G. Stéphane (editors)
Statistics for Astrophysics: Clustering and Classification, EAS Publications Series, EDP Sciences, Les Houches, France, 2016, vol. 77.
https://hal.archives-ouvertes.fr/hal-01324665
[55]
S. Doyle, F. Forbes, M. Dojat.
Automatic multiple sclerosis lesion segmentation with P-LOCUS, in: Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge Using a Data Managementand Processing Infrastructure — MICCAI-MSSEG, 2016, pp. 17-21.
http://www.hal.inserm.fr/inserm-01417434
[56]
F. Forbes.
Modelling structured data with probabilistic graphical models, in: Statistics for Astrophysics- Classification and Clustering, EDP Sciences, EAS Publication series, 2016, vol. 77, pp. 2016 - 2016.
https://hal.archives-ouvertes.fr/hal-01423613
[57]
S. Girard, J. Saracco.
Supervised and unsupervised classification using mixture models, in: Statistics for Astrophysics: Clustering and Classification, D. Fraix-Burnet, S. Girard (editors), EAS Publications Series, EDP Sciences, May 2016, vol. 77, pp. 69-90.
https://hal.archives-ouvertes.fr/hal-01417514
[58]
C. Maggia, S. Doyle, F. Forbes, O. Heck, I. Troprès, C. Berthet, Y. Teyssier, L. Velly, J.-F. Payen, M. Dojat.
Assessment of Tissue Injury in Severe Brain Trauma, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science, Springer International Publishing, 2016, vol. 9556, pp. 57-68.
https://hal.archives-ouvertes.fr/hal-01423467

Other Publications

[59]
M. Albughdadi, L. Chaari, J.-Y. Tourneret, F. Forbes, P. Ciuciu.
Hemodynamic Brain Parcellation Using A Non-Parametric Bayesian Approach, February 2016, working paper or preprint.
https://hal.inria.fr/hal-01275622
[60]
R. Azaïs, J.-B. Durand, C. Godin.
Approximation of trees by self-nested trees, September 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01294013
[61]
R. Azaïs, J.-B. Durand, C. Godin.
Lossy compression of unordered rooted trees, March 2016, DCC 2016 - Data Compression Conference , Poster. [ DOI : 10.1109/DCC.2016.73 ]
https://hal.inria.fr/hal-01394707
[62]
L. Chaari, S. Badillo, T. Vincent, G. Dehaene-Lambertz, F. Forbes, P. Ciuciu.
Subject-level Joint Parcellation-Detection-Estimation in fMRI, January 2016, working paper or preprint.
https://hal.inria.fr/hal-01255465
[63]
A. Daouia, S. Girard, G. Stupfler.
Estimation of Tail Risk based on Extreme Expectiles, June 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01142130
[64]
J. El Methni, L. Gardes, S. Girard.
Kernel estimation of extreme regression risk measures, November 2016, working paper or preprint.
https://hal.inria.fr/hal-01393519
[65]
P. Fernique, J. Legrand, J.-B. Durand, Y. Guédon.
Semi-parametric Markov Tree for cell lineage analysis, June 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01286298
[66]
P. Fernique, J. Peyhardi, J.-B. Durand.
Multinomial distributions for the parametric modeling of multivariate count data, April 2016, working paper or preprint.
https://hal.inria.fr/hal-01286171
[67]
F. Forbes.
Introduction to statistical methods in signal and image processing, July 2016, Lecture.
https://hal.archives-ouvertes.fr/cel-01423624
[68]
M. Lopes, M. Fauvel, S. Girard, D. Sheeren, M. Lang.
High Dimensional Kullback-Leibler Divergence for grassland object-oriented classification from high resolution satellite image time series, May 2016, Living Planet Symposium, Poster.
https://hal.archives-ouvertes.fr/hal-01326865
[69]
M. Lopes, M. Fauvel, S. Girard, D. Sheeren, M. Lang.
High Dimensional Kullback-Leibler Divergence for grassland object-oriented classification from high resolution satellite image time series, March 2016, 4ème Journée Thématique du Programme National de Télédétection Spatiale (PNTS), Poster.
https://hal.archives-ouvertes.fr/hal-01366221
[70]
M. Lopes, M. M. Fauvel, S. Girard, D. Sheeren.
Object-based classification from high resolution satellite image time series with Gaussian mean map kernels: Application to grassland management practices, January 2017, working paper or preprint.
https://hal.inria.fr/hal-01424929
[71]
E. Perthame, F. Forbes, A. Deleforge.
Inverse regression approach to robust non-linear high-to-low dimensional mapping, July 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01347455
[72]
E. Perthame, C.-F. Sheu, D. Causeur.
Signal identification in ERP data by decorrelated Higher Criticism Thresholding, May 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01310739
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Multi-session extension of the joint-detection framework in fMRI, in: ISBI 2013 - International Symposium on BIomedical Imaging: From Nano to Macro, San Fransisco, United States, IEEE, April 2013, pp. 1512-1515. [ DOI : 10.1109/ISBI.2013.6556822 ]
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[75]
A. Deleforge, F. Forbes, R. Horaud.
Acoustic Space Learning for Sound-Source Separation and Localization on Binaural Manifolds, in: International Journal of Neural Systems, February 2015, vol. 25, no 1, 21p p. [ DOI : 10.1142/S0129065714400036 ]
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Quantifying the degree of self-nestedness of trees: application to the structural analysis of plants, in: IEEE/ACM Transactions in Computational Biology and Bioinformatics, 2010, vol. 7, pp. 688–703.
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P. Mesejo, S. Saillet, O. David, C. Bénar, J. M. Warnking, F. Forbes.
Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization, in: 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'15), Munich, Germany, October 2015, vol. Part II, pp. 528-535. [ DOI : 10.1007/978-3-319-24571-3_63 ]
https://hal.inria.fr/hal-01221126
[82]
P. Mesejo, S. Saillet, O. David, C. Bénar, J. M. Warnking, F. Forbes.
A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data, in: IEEE Journal of Selected Topics in Signal Processing, March 2016, vol. 10, no 2, pp. 416-427. [ DOI : 10.1109/JSTSP.2015.2502553 ]
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https://hal.archives-ouvertes.fr/hal-01179842