Members
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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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]
B. Barroca, P. Bernadara, S. Girard, G. Mazo.
Considering hazard estimation uncertain in urban resilience strategies, in: Natural Hazards and Earth System Sciences, 2015, vol. 15, pp. 25-34. [ DOI : 10.5194/nhess-15-25-2015 ]
https://hal.archives-ouvertes.fr/hal-01100539
[12]
C. Bouveyron, M. Fauvel, S. Girard.
Kernel discriminant analysis and clustering with parsimonious Gaussian process models, in: Statistics and Computing, 2014, 33 pages - arXiv:1204.4021, forthcoming.
https://hal.archives-ouvertes.fr/hal-00687304
[13]
M. Chavent, S. Girard, V. Kuentz, B. Liquet, T. M. N. Nguyen, J. Saracco.
A sliced inverse regression approach for data stream, in: Computational Statistics, 2014, vol. 29, pp. 1129–1152. [ DOI : 10.1007/s00180-014-0483-4 ]
https://hal.inria.fr/hal-00688609
[14]
M. Chini, A. Chiancone, S. Stramondo.
Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification, in: Pattern Recognition Letters, November 2014, vol. 49, pp. 214-223. [ DOI : 10.1016/j.patrec.2014.07.012 ]
https://hal.archives-ouvertes.fr/hal-01065938
[15]
R. Coudret, S. Girard, J. Saracco.
A new sliced inverse regression method for multivariate response, in: Computational Statistics and Data Analysis, September 2014, vol. 77, pp. 285-299. [ DOI : 10.1016/j.csda.2014.03.006 ]
https://hal.inria.fr/hal-00714981
[16]
A. Daouia, S. Girard, A. Guillou.
A Gamma-moment approach to monotonic boundaries estimation: with applications in econometric and nuclear fields, in: Journal of Econometrics, February 2014, vol. 178, no 2, pp. 727-740. [ DOI : 10.1016/j.jeconom.2013.10.013 ]
https://hal.inria.fr/hal-00737732
[17]
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
[18]
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, 21 p. [ DOI : 10.1142/S0129065714400036 ]
https://hal.inria.fr/hal-00960796
[19]
J.-B. Durand, Y. Guédon.
Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models, in: Statistics and Computing, 2014, 19 p. [ DOI : 10.1007/s11222-014-9494-9 ]
https://hal.inria.fr/hal-01090836
[20]
J. El Methni, L. Gardes, S. Girard.
Non-parametric estimation of extreme risk measures from conditional heavy-tailed distributions, in: Scandinavian Journal of Statistics, 2014, vol. 41, no 4, pp. 988–1012. [ DOI : 10.1111/sjos.12078 ]
https://hal.archives-ouvertes.fr/hal-00830647
[21]
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
[22]
S. Girard, A. Guillou, G. Stupfler.
Uniform strong consistency of a frontier estimator using kernel regression on high order moments, in: ESAIM: Probability and Statistics, 2014, vol. 18, pp. 642–666. [ DOI : 10.1051/ps/2013050 ]
https://hal.archives-ouvertes.fr/hal-00764425
[23]
M.-J. Martinez, E. Holian.
An alternative estimation approach for the heterogeneity linear mixed model, in: Communications in Statistics - Simulation and Computation, 2014, vol. 43, no 10, pp. 2628-2638. [ DOI : 10.1080/03610918.2012.762389 ]
https://hal.archives-ouvertes.fr/hal-00926620
[24]
B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M.-A. Weber, T. Arbel, B. Avants, N. Ayache, P. Buendia, L. Collins, N. Cordier, J. Corso, A. Criminisi, T. Das, H. Delingette, C. Demiralp, C. Durst, M. Dojat, S. Doyle, J. Festa, F. Forbes, E. Geremia, B. Glocker, P. Golland, X. Guo, A. Hamamci, K. Iftekharuddin, R. Jena, N. John, E. Konukoglu, D. Lashkari, J. Antonio Mariz, R. Meier, S. Pereira, D. Precup, S. J. Price, T. Riklin-Raviv, S. Reza, M. Ryan, L. Schwartz, H.-C. Shin, J. Shotton, C. Silva, N. Sousa, N. Subbanna, G. Szekely, T. Taylor, O. Thomas, N. Tustison, G. Unal, F. Vasseur, M. Wintermark, D. Hye Ye, L. Zhao, B. Zhao, D. Zikic, M. Prastawa, M. Reyes, K. Van Leemput.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), in: IEEE Transactions on Medical Imaging, 2014, 33 p. [ DOI : 10.1109/TMI.2014.2377694 ]
https://hal.inria.fr/hal-00935640
[25]
A. Nazin, S. Girard.
L1-optimal linear programming estimatorfor periodic frontier functions with Holder continuous derivative, in: Automation and Remote Control / Avtomatika i Telemekhanika, 2014, vol. 75, no 12, pp. 2152-2169.
https://hal.archives-ouvertes.fr/hal-01066739
[26]
T. Vincent, S. Badillo, L. Risser, L. Chaari, C. Bakhous, F. Forbes, P. Ciuciu.
Frontiers in Neuroinformatics Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF, in: Frontiers in Neuroscience, April 2014, vol. 8, no Article 67, 23 p. [ DOI : 10.3389/fnins.2014.00067 ]
https://hal.inria.fr/hal-01084249

Invited Conferences

[27]
F. Forbes, A. Deleforge, R. Horaud.
High dimensional regression with Gaussian mixtures and partially latent response variables, in: SuSTaIn Image Processing workshop "high-dimensional stochastic simulation and optimisation in image processing", Bristol, United Kingdom, August 2014. [ DOI : 10.1007/s11222-014-9461-5 ]
https://hal.inria.fr/hal-01107604
[28]
F. Forbes, A. Frau-Pascual, T. Vincent, J. Sloboda, P. Ciuciu.
Physiologically informed Bayesian analysis of ASL fMRI data, in: Statistical Challenges in Neuroscience workshop, Warwick, United Kingdom, September 2014.
https://hal.inria.fr/hal-01107613
[29]
F. Forbes, D. Wraith.
Robust mixture modelling using skewed multivariate distributions with variable amounts of tailweight, in: 7th International Conference of the ERCIM WG on Computing and Statistics, Pise, Italy, October 2014.
https://hal.inria.fr/hal-01107622

International Conferences with Proceedings

[30]
M. Albughdadi, L. Chaari, F. Forbes, J.-Y. Tourneret, P. Ciuciu.
Model Selection for Hemodynamic Brain Parcellation in fMRI, in: EUSIPCO - 22nd European Signal Processing Conference, Lisbon, Portugal, IEEE, September 2014, pp. 31 - 35.
https://hal.inria.fr/hal-01107475
[31]
A. Deleforge, F. Forbes, R. Horaud.
Hyper-spectral Image Analysis with Partially-Latent Regression, in: 22nd European Signal Processing Conference, Lisbon, Portugal, September 2014.
https://hal.archives-ouvertes.fr/hal-01019360
[32]
S. Doyle, B. Lemasson, F. Vasseur, P. Bourdillion, F. Ducray, J. Honnorat, L. Guilloton, J. Guyotat, C. Remy, F. Forbes, F. Cotton, E. Barbier, M. Dojat.
Comparison of manual versus automatic delineation of low-grade gliomas based on MR brain scans, in: Organization for Human Brain Mapping (OHBM) 2014 Annual meeting, Hambourg, Germany, June 2014.
https://hal.inria.fr/hal-01107700
[33]
J.-B. Durand, Y. Guédon.
Quantifying and localizing state uncertainty in hidden Markov models using conditional entropy profiles, in: COMPSTAT 2014 - 21st International Conference on Computational Statistics, Genève, Switzerland, M. Gilli, G. González-Rodríguez, A. Nieto-Reyes (editors), Université de Genève, August 2014, pp. 213-221, ISBN 978-2-8399-1347-8. Please check publisher.
https://hal.inria.fr/hal-01058278
[34]
J. El Methni, S. Girard, L. Gardes.
Kernel estimation of extreme risk measures for all domains of attraction, in: COMPSTAT 2014 - 21st International Conference on Computational Statistics, Geneva, Switzerland, August 2014, CDROM.
https://hal.archives-ouvertes.fr/hal-01062363
[35]
M. Fauvel, C. Bouveyron, S. Girard.
Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images, in: ICASSP - IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, IEEE, May 2014, pp. 2913-2916. [ DOI : 10.1109/ICASSP.2014.6854133 ]
https://hal.archives-ouvertes.fr/hal-01062378
[36]
P. Fernique, J.-B. Durand, Y. Guédon.
Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes, in: COMPSTAT - 21st International Conference on Computational Statistics, Geneva, Switzerland, Proceedings of COMPSTAT 2014, The International Association for Statistical Computing (IASC), August 2014.
https://hal.inria.fr/hal-01084524
[37]
P. Fernique, J.-B. Durand, Y. Guédon.
Learning Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes, in: COMPSTAT 2014 - 21st International Conference on Computational Statistics, Genève, Switzerland, M. Gilli, G. González-Rodríguez, A. Nieto-Reyes (editors), Université de Genève, August 2014, pp. 579-586, ISBN 978-2-8399-1347-8. Please check publisher.
https://hal.inria.fr/hal-01058284
[38]
A. Frau-Pascual, T. Vincent, F. Forbes, P. Ciuciu.
Hemodynamically informed parcellation of cerebral FMRI data, in: ICASSP - IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, IEEE, May 2014, pp. 2079-2083. [ DOI : 10.1109/ICASSP.2014.6853965 ]
https://hal.inria.fr/hal-01100186
[39]
A. Frau-Pascual, T. Vincent, J. Sloboda, P. Ciuciu, F. Forbes.
Physiologically Informed Bayesian Analysis of ASL fMRI Data, in: BAMBI 2014 - First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, Boston, United States, M. J. Cardoso, I. Simpson, T. Arbel, D. Precup, A. Ribbens (editors), Lecture Notes in Computer Science, Springer International Publishing, September 2014, vol. 8677, pp. 37 - 48. [ DOI : 10.1007/978-3-319-12289-2_4 ]
https://hal.inria.fr/hal-01100266
[40]
I.-D. Gebru, X. Alameda-Pineda, R. Horaud, F. Forbes.
Audio-Visual Speaker Localization via Weighted Clustering, in: IEEE Workshop on Machine Learning for Signal Processing, Reims, France, September 2014, 6 p.
https://hal.archives-ouvertes.fr/hal-01053732
[41]
S. Girard, G. Stupfler.
Extreme geometric quantiles, in: 7th International Conference of the ERCIM WG on Computing and Statistics, Pise, Italy, December 2014.
https://hal.archives-ouvertes.fr/hal-01093048
[42]
G. Mazo, S. Girard, F. Forbes.
A flexible, tractable class of copulas and its estimation, in: COMPSTAT 2014 - 21st International Conference on Computational Statistics, Geneva, Switzerland, August 2014.
https://hal.archives-ouvertes.fr/hal-01062481
[43]
A. Roche, F. Forbes.
Partial volume estimation in brain MRI revisited, in: MICCAI 2014 - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, United States, P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe (editors), Springer, September 2014, vol. 8673, pp. 771-778. [ DOI : 10.1007/978-3-319-10404-1_96 ]
https://hal.inria.fr/hal-01107469
[44]
G. Stupfler, S. Girard.
On the asymptotic behaviour of extreme geometric quantiles, in: Workshop on Extreme Value Theory, with an emphasis on spatial and temporal aspects, Besancon, France, November 2014.
https://hal.archives-ouvertes.fr/hal-01086054

National Conferences with Proceedings

[45]
A. Arnaud, F. Forbes, N. Coquery, E. Barbier, B. Lemasson.
Mélanges de lois de Student multivariées généralisées : application à la caractérisation de tumeurs par IRM multiparamétrique, in: 2ème congrès de la SFRMBM (Société Française de Résonance Magnétique en Biologie et Médecine), Grenoble, France, March 2015.
https://hal.inria.fr/hal-01107483
[46]
A. Chiancone, S. Girard, J. Chanussot.
Collaborative Sliced Inverse Regression, in: Rencontres d'Astrostatistique, Grenoble, France, November 2014.
https://hal.archives-ouvertes.fr/hal-01086931
[47]
S. Doyle, B. Lemasson, F. Vasseur, P. Bourdillion, F. Ducray, J. Honnorat, L. Guilloton, J. Guyotat, C. Remy, F. Forbes, F. Cotton, E. Barbier, M. Dojat.
Segmentation des tumeurs cérébrales de bas grade par une approche bayésienne : délinéation manuelle versus automatique, in: 2ème congrès de la SFRMBM (Société Française de Résonance Magnétique en Biologie et Médecine), Grenoble, France, March 2015.
https://hal.inria.fr/hal-01107520
[48]
J.-B. Durand, Y. Guédon.
Quantification de l'incertitude sur la structure latente dans des modèles de Markov cachés, in: 46èmes Journées de Statistique, Rennes, France, June 2014.
https://hal.inria.fr/hal-01058317
[49]
P. Fernique, J.-B. Durand, Y. Guédon.
Modèles graphiques paramétriques pour la modélisation des lois de génération dans des processus de branchement multitypes, in: 46èmes Journées de Statistique, Rennes, France, June 2014.
https://hal.inria.fr/hal-01058313
[50]
F. Forbes, A. Deleforge, R. Horaud.
High dimensional regression with Gaussian mixtures and partially latent response variables: Application to hyper-­spectral image analysis, in: Rencontre d'Astrostatistique, Grenoble, France, November 2014.
https://hal.inria.fr/hal-01107616
[51]
S. Sylla, S. Girard, A. K. Diongue, A. Diallo, C. Sokhna.
Classification supervisee par modele de melange: Application aux diagnostics par autopsie verbale, in: 46èmes Journées de Statistique organisées par la Société Française de Statistique, Rennes, France, June 2014.
https://hal.archives-ouvertes.fr/hal-01090014

Scientific Books (or Scientific Book chapters)

[52]
E. H. Deme, S. Girard, A. Guillou.
Reduced-bias estimator of the Conditional Tail Expectation of heavy-tailed distributions, in: Mathematical Statistics and Limit Theorems, D. Mason (editor), Springer, 2014.
https://hal.inria.fr/hal-00823260
[53]
S. Girard, S. Louhichi.
On the strong consistency of the kernel estimator of extreme conditional quantiles, in: Recent advances in statistical methodology and its application, E. O. Said (editor), Springer, 2014.
https://hal.inria.fr/hal-01058390
[54]
M.-J. Martinez, J. Hinde.
Random effects ordinal time models for grouped toxicological data from a biological control assay, in: Statistical Modelling in Biostatistics and Bioinformatics: Selected papers, G. MacKenzie, D. Peng (editors), Contributions to Statistics, Springer, May 2014, pp. 45-58. [ DOI : 10.1007/978-3-319-04579-5_5 ]
https://hal.archives-ouvertes.fr/hal-00943962

Other Publications

[55]
C. Amblard, S. Girard, L. Menneteau.
Bivariate copulas defined from matrices, 2014.
https://hal.archives-ouvertes.fr/hal-00875303
[56]
C. Bazzoli, F. Letué, M.-J. Martinez.
Modelling finger force produced from different tasks using linear mixed models with lme R function, June 2014.
https://hal.archives-ouvertes.fr/hal-00998910
[57]
L. Gardes, S. Girard.
Nonparametric estimation of the conditional tail copula, March 2014.
https://hal.archives-ouvertes.fr/hal-00964514
[58]
L. Gardes, S. Girard.
On the estimation of the functional Weibull tail-coefficient, 2014.
https://hal.archives-ouvertes.fr/hal-01063569
[59]
S. Girard.
An introduction to SIR: A statistical method for dimension reduction in multivariate regression, 2014.
https://hal.archives-ouvertes.fr/hal-01058721
[60]
S. Girard, S. Louhichi.
On the strong consistency of the kernel estimator of extreme conditional quantiles, March 2014.
https://hal.archives-ouvertes.fr/hal-00956351
[61]
S. Girard, J. Saracco.
An introduction to dimension reduction in nonparametric kernel regression, 2014.
https://hal.archives-ouvertes.fr/hal-00977512
[62]
S. Girard, G. Stupfler.
Asymptotic behaviour of extreme geometric quantiles and their estimation under moment conditions, September 2014.
https://hal.inria.fr/hal-01060985
[63]
S. Girard, G. Stupfler.
Intriguing properties of extreme geometric quantiles, February 2014.
https://hal.inria.fr/hal-00865767
[64]
G. Mazo, S. Girard, F. Forbes.
A class of multivariate copulas based on products of bivariate copulas, July 2014.
https://hal.archives-ouvertes.fr/hal-00910775
[65]
G. Mazo, S. Girard, F. Forbes.
A flexible and tractable class of one-factor copulas, April 2014.
https://hal.archives-ouvertes.fr/hal-00979147
[66]
G. Mazo, S. Girard, F. Forbes.
Weighted least-squares inference based on dependence coefficients for multivariate copulas, April 2014.
https://hal.archives-ouvertes.fr/hal-00979151
References in notes
[67]
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
[68]
J.-B. Durand, P. Gonçalvès, Y. Guédon.
Computational Methods for Hidden Markov Tree Models – An Application to Wavelet Trees, in: IEEE Transactions on Signal Processing, September 2004, vol. 52, no 9, pp. 2551–2560.
[69]
P. Embrechts, C. Klüppelberg, T. Mikosh.
Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
[70]
P. Fernique.
A statistical modeling framework for analyzing tree-indexed data, Université de Montpellier 2, December 2014.
http://tel.archives-ouvertes.fr/tel-01095420
[71]
F. Ferraty, P. Vieu.
Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
[72]
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.
[73]
D. Koller, N. Friedman.
Probabilistic graphical models: principles and techniques, MIT press, 2009.
[74]
K. Li.
Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, pp. 316–327.
[75]
R. Nelsen.
An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.
[76]
J. Simola, J. Salojärvi, I. Kojo.
Using hidden Markov model to uncover processing states from eye movements in information search tasks, in: Cognitive Systems Research, Oct 2008, vol. 9, no 4, pp. 237-251.
[77]
T. Vincent, J. Warnking, M. Villien, A. Krainik, P. Ciuciu, F. Forbes.
Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data, in: MICCAI 2013 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, Nagoya, Japan, K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab (editors), Lecture Notes in Computer Science, Springer, June 2013, vol. 8150, pp. 616-623. [ DOI : 10.1007/978-3-642-40763-5_76 ]
http://hal.inria.fr/hal-00854437