Bibliography

Major publications by the team in recent years

• 1C. 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, pp. 1–15.
• 2J. 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.
• 3C. Bouveyron, S. Girard, C. Schmid.
  High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, pp. 502–519.
• 4C. Bouveyron, S. Girard, C. Schmid.
  High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, n^o 14.
• 5L. 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, n^o 5, pp. 821-837. [ DOI : 10.1109/TMI.2012.2225636 ]
  http://hal.inria.fr/inserm-00753873
• 6A. 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
• 7F. 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, n^o 3, pp. 824-837.
• 8F. 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, n^o 6, pp. 971-984. [ DOI : 10.1007/s11222-013-9414-4 ]
  https://hal.inria.fr/hal-00823451
• 9S. Girard.
  A Hill type estimate of the Weibull tail-coefficient, in: Communication in Statistics - Theory and Methods, 2004, vol. 33, n^o 2, pp. 205–234.
• 10S. Girard, P. Jacob.
  Extreme values and Haar series estimates of point process boundaries, in: Scandinavian Journal of Statistics, 2003, vol. 30, n^o 2, pp. 369–384.

Publications of the year

Articles in International Peer-Reviewed Journals

• 11B. 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
• 12C. Bazzoli, F. Letué, M.-J. Martinez.
  Modelling finger force produced from different tasks using linear mixed models with lme R function, in: Journal of Case Studies in Business, Industry and Government Statistics (CSBIGS), 2015, vol. 6, n^o 1, pp. 16-36.
  https://hal.archives-ouvertes.fr/hal-00998910
• 13C. Bouveyron, M. Fauvel, S. Girard.
  Kernel discriminant analysis and clustering with parsimonious Gaussian process models, in: Statistics and Computing, 2015, vol. 25, n^o 6, pp. 1143–1162, 33 pages - arXiv:1204.4021. [ DOI : 10.1007/s11222-014-9505-x ]
  https://hal.archives-ouvertes.fr/hal-00687304
• 14A. Chiancone, S. Girard, J. Chanussot.
  Collaborative Sliced Inverse Regression, in: Communication in Statistics - Theory and Methods, 2015, forthcoming.
  https://hal.inria.fr/hal-01158061
• 15A. Deleforge, F. Forbes, S. Ba, R. Horaud.
  Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies, in: IEEE Journal on Selected Topics in Signal Processing, September 2015, vol. 9, n^o 6, pp. 1037-1048. [ DOI : 10.1109/JSTSP.2015.2416677 ]
  https://hal.inria.fr/hal-01136465
• 16A. 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, n^o 1, 21 p. [ DOI : 10.1142/S0129065714400036 ]
  https://hal.inria.fr/hal-00960796
• 17A. Deleforge, F. Forbes, R. Horaud.
  High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables, in: Statistics and Computing, September 2015, vol. 25, n^o 5, pp. 893-911. [ DOI : 10.1007/s11222-014-9461-5 ]
  https://hal.inria.fr/hal-00863468
• 18J.-B. Durand, Y. Guédon.
  Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models, in: Statistics and Computing, 2016, vol. 26, n^o 1, pp. 549-567. [ DOI : 10.1007/s11222-014-9494-9 ]
  https://hal.inria.fr/hal-01090836
• 19F. Durante, S. Girard, G. Mazo.
  Marshall–Olkin type copulas generated by a global shock, in: Journal of Applied and Computational Mathematics, 2016, vol. 296, pp. 638–648.
  https://hal.archives-ouvertes.fr/hal-01138228
• 20J. El Methni, L. Gardes, S. Girard.
  Estimation of risk measures for extreme pluviometrical measurements in the Cévennes-Vivarais region, in: La Houille Blanche, September 2015, n^o 4, pp. 46-51.
  https://hal.archives-ouvertes.fr/hal-01212105
• 21M. Fauvel, C. Bouveyron, S. Girard.
  Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images, in: IEEE Geosciences and Remote Sensing Letters, 2015, vol. 12, pp. 2423–2427. [ DOI : 10.1109/LGRS.2015.2481321 ]
  https://hal.archives-ouvertes.fr/hal-01203269
• 22L. Gardes, S. Girard.
  Nonparametric estimation of the conditional tail copula, in: Journal of Multivariate Analysis, 2015, vol. 137, pp. 1–16.
  https://hal.archives-ouvertes.fr/hal-00964514
• 23L. Gardes, S. Girard.
  On the estimation of the functional Weibull tail-coefficient, in: Journal of Multivariate Analysis, 2015, forthcoming.
  https://hal.archives-ouvertes.fr/hal-01063569
• 24S. Girard, G. Stupfler.
  Extreme geometric quantiles in a multivariate regular variation framework, in: Extremes, 2015, vol. 18, n^o 4, pp. 629–663.
  https://hal.archives-ouvertes.fr/hal-01155112
• 25S. Girard, G. Stupfler.
  Intriguing properties of extreme geometric quantiles, in: REVSTAT - Statistical Journal, 2015, forthcoming.
  https://hal.inria.fr/hal-00865767
• 26G. Mazo, S. Girard, F. Forbes.
  A class of multivariate copulas based on products of bivariate copulas, in: Journal of Multivariate Analysis, September 2015, vol. 140, pp. 363-376.
  https://hal.archives-ouvertes.fr/hal-00910775
• 27G. Mazo, S. Girard, F. Forbes.
  A flexible and tractable class of one-factor copulas, in: Statistics and Computing, June 2015, forthcoming.
  https://hal.archives-ouvertes.fr/hal-00979147
• 28G. Mazo, S. Girard, F. Forbes.
  Weighted least-squares inference for multivariate copulas based on dependence coefficients, in: ESAIM: Probability and Statistics, October 2015, vol. 19, pp. 746 - 765. [ DOI : 10.1051/ps/2015014 ]
  https://hal.archives-ouvertes.fr/hal-00979151
• 29P. 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.
  https://hal.inria.fr/hal-01221115
• 30S. N. Sylla, S. Girard, A. K. Diongue, A. Diallo, C. Sokhna.
  A classification method for binary predictors combining similarity measures and mixture models, in: Dependence Modeling, 2015, vol. 3, pp. 240–255.
  https://hal.inria.fr/hal-01158043
• 31D. Wraith, F. Forbes.
  Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering, in: Computational Statistics & Data Analysis, 2015, vol. 90, pp. 61-73.
  https://hal.archives-ouvertes.fr/hal-01254178

Invited Conferences

• 32J. El Methni, L. Gardes, S. Girard.
  Estimation non-paramétrique de mesures de risque pour des lois conditionnelles à queues lourdes avec application à des extrêmes pluviométriques, in: Congrès SMAI, Les Karellis, France, June 2015.
  https://hal.archives-ouvertes.fr/hal-01168790
• 33G. Stupfler, S. Girard.
  On the asymptotic behaviour of extreme geometric quantiles, in: 9th International Conference on Extreme Value Analysis, Ann Arbor, United States, 9th International Conference on Extreme Value Analysis, June 2015.
  https://hal.archives-ouvertes.fr/hal-01168521

International Conferences with Proceedings

• 34L. Amsaleg, C. Oussama, T. Furon, S. Girard, M. E. Houle, K.-I. Kawarabayashi.
  Estimating Local Intrinsic Dimensionality, in: 21st Conf. on Knowledge Discovery and Data Mining, KDD2015, Sidney, Australia, ACM, August 2015.
  https://hal.inria.fr/hal-01159217
• 35A. Arnaud, F. Forbes, B. Lemasson, E. L. Barbier.
  Tumor classification and prediction using robust multivariate clustering of multiparametric MRI, in: International Society for Magnetic Resonance in Medicine, Toronto, Canada, May 2015.
  https://hal.archives-ouvertes.fr/hal-01253584
• 36F. Forbes, A. Frau-Pascual, P. Ciuciu.
  Méthode d'approximation variationnelle pour l'analyse de données d'IRM fonctionnelle acquise par Arterial Spin Labelling, in: GRETSI, Lyon, France, 2015.
  https://hal.archives-ouvertes.fr/hal-01254176
• 37A. Frau-Pascual, F. Forbes, P. Ciuciu.
  Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis, in: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Munich, Germany, N. Navab, J. Hornegger, W. M. Wells III, A. F. Frangi (editors), Lecture Notes in Computer Science, Springer, October 2015, vol. 9349, n^o ISBN 978-3-319-24552-2. [ DOI : 10.1007/978-3-319-24553-9_11 ]
  https://hal.archives-ouvertes.fr/hal-01249018
• 38A. Frau-Pascual, F. Forbes, P. Ciuciu.
  Physiological models comparison for the analysis of ASL FMRI data, in: 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, New York, United States, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA, April 16-19, 2015. IEEE 2015, April 2015, pp. 1348-1351. [ DOI : 10.1109/ISBI.2015.7164125 ]
  https://hal.archives-ouvertes.fr/hal-01249014
• 39A. Frau-Pascual, F. Forbes, P. Ciuciu.
  Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data, in: 2015 International Workshop on Pattern Recognition in NeuroImaging, Stanford, CA, United States, 2015 International Workshop on Pattern Recognition in NeuroImaging, Stanford, CA, USA, June 10-12, 2015. IEEE 2015, June 2015, pp. 57-60. [ DOI : 10.1109/PRNI.2015.12 ]
  https://hal.archives-ouvertes.fr/hal-01249015
• 40P. 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

National Conferences with Proceedings

• 41A. 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
• 42A. Arnaud, F. Forbes, B. Lemasson, E. Barbier.
  Paquet R pour l'estimation d'un mélange de lois de Student multivariées à échelles multiples, in: Quatrièmes Rencontres R, Grenoble, France, June 2015.
  https://hal.archives-ouvertes.fr/hal-01253593
• 43A. Arnaud, F. Forbes, B. Lemasson, E. Barbier, N. Coquery.
  Mélanges de lois de Student à Échelles Multiples pour la caractérisation de tumeurs par IRM multiparamétrique, in: 47èmes Journées de Statistique de la SFdS, Lille, France, June 2015.
  https://hal.archives-ouvertes.fr/hal-01253588
• 44S. 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
• 45P. Fernique, J.-B. Durand, Y. Guédon.
  Détection de motifs disruptifs au sein de plantes : une approche de quotientement/classification d'arborescences, in: 47èmes Journées de Statistique, Lille, France, 47èmes Journées de Statistique, Lille, Société Française de Statistique, June 2015.
  https://hal.inria.fr/hal-01240305
• 46S. N. Sylla, S. Girard, A. K. Diongue, A. Diallo, C. Sokhna.
  Classification de données binaires via l'introduction de mesures de similarités dans les modèles de mélange, in: 47èmes Journées de Statistique organisées par la Société Française de Statistique, Lille, France, 2015.
  https://hal.archives-ouvertes.fr/hal-01168530

Conferences without Proceedings

• 47A. Arnaud, F. Forbes, B. Lemasson, E. Barbier.
  Multivariate Multi-scaled Student Distributions : brain tumor characterization from multiparametric MRI, in: Statistique Mathématique et Applications 2015, Fréjus, France, August 2015.
  https://hal.archives-ouvertes.fr/hal-01253595
• 48A. Chiancone, S. Girard, J. Chanussot.
  Collaborative Sliced Inverse Regression, in: 20th Young Statisticians Meeting, Vorau, Austria, 2015.
  https://hal.archives-ouvertes.fr/hal-01221010
• 49S. Girard, A. Rivera, M. Stehlik, S. Torres-Leiva.
  Extreme value modelling of some glacial processes in Chilean Andes, in: International Conference on Risk Analysis, Barcelona, Spain, May 2015.
  https://hal.archives-ouvertes.fr/hal-01168766

Scientific Books (or Scientific Book chapters)

• 50P. Ciuciu, F. Forbes, T. Vincent, L. Chaari.
  Chapter 7 Joint Detection-Estimation in Functional MRI, in: in Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing, J.-F. Giovannelli, J. Idier (editors), John Wiley and Sons, 2015, pp. 160-200.
  https://hal.archives-ouvertes.fr/hal-01254177
• 51E. H. Deme, S. Girard, A. Guillou.
  Reduced-bias estimator of the Conditional Tail Expectation of heavy-tailed distributions, in: Mathematical Statistics and Limit Theorems, M. Hallin (editor), Springer, 2015, pp. 105–123.
  https://hal.inria.fr/hal-00823260
• 52F. Durante, S. Girard, G. Mazo.
  Copulas based on Marshall–Olkin machinery, in: Marshall-Olkin Distributions. Advances in Theory and Applications, U. Cherubini (editor), Springer Proceedings in Mathematics and Statistics, Springer, 2015, vol. 141, pp. 15–31.
  https://hal.archives-ouvertes.fr/hal-01153150
• 53S. Girard, S. Louhichi.
  On the strong consistency of the kernel estimator of extreme conditional quantiles, in: Functional Statistics and Applications, E. Ould Said (editor), Contributions to Statistics, Springer, 2015, pp. 59–77.
  https://hal.inria.fr/hal-01058390

Other Publications

• 54A. Arnaud, F. Forbes, N. Coquery, B. Lemasson, E. Barbier.
  Mélanges de lois de Student multivariées généralisées : application a la caractérisation de tumeurs par IRM multiparamétrique, April 2015, Forum de la Recherche en Cancérologie Rhône-Alpes Auvergne, Poster.
  https://hal.archives-ouvertes.fr/hal-01253576
• 55R. Azaïs, J.-B. Durand, C. Godin.
  Lossy compression of unordered rooted trees, December 2015, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01236088
• 56L. Chaari, S. Badillo, T. Vincent, G. Dehaene-Lambertz, F. Forbes, P. Ciuciu.
  Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework, November 2015, Submitted to IEEE Transactions on Medical Imaging. [ DOI : 10.1007/978-3-642-33454-2_23 ]
  https://hal.archives-ouvertes.fr/hal-01228007
• 57L. 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
• 58A. Chiancone.
  What's wrong with classes? The theory of Knowledge, February 2015, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01113112
• 59A. Daouia, S. Girard, G. Stupfler.
  Assessing coherent Value-at-Risk and expected shortfall with extreme expectiles, April 2015, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01142130
• 60S. Girard, J. Saracco.
  Supervised and unsupervised classification using mixture models, December 2015, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01241818

References in notes

• 61C. 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
• 62P. Embrechts, C. Klüppelberg, T. Mikosh.
  Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
• 63F. Ferraty, P. Vieu.
  Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
• 64S. 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.
• 65C. Godin, P. Ferraro.
  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.
  http://www-sop.inria.fr/virtualplants/Publications/2010/GF10
• 66K. Li.
  Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, pp. 316–327.
• 67R. Nelsen.
  An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.
• 68J. 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, n^o 4, pp. 237-251.