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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, no 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, no 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, no 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, no 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, no 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, no 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, no 2, pp. 369–384.
Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 14L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K.-I. Kawarabayashi, M. Nett.

    Extreme-value-theoretic estimation of local intrinsic dimensionality, in: Data Mining and Knowledge Discovery, November 2018, vol. 32, no 6, pp. 1768–1805. [ DOI : 10.1007/s10618-018-0578-6 ]

    https://hal.archives-ouvertes.fr/hal-01864580
  • 15J. Arbel, P. De Blasi, I. Prünster.

    Stochastic approximations to the Pitman-Yor process, in: Bayesian Analysis, 2018, pp. 1-19. [ DOI : 10.1214/18-BA1127 ]

    https://hal.archives-ouvertes.fr/hal-01950654
  • 16A. Arnaud, F. Forbes, N. Coquery, N. Collomb, B. L. Lemasson, E. L. Barbier.

    Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data, in: IEEE Transactions on Medical Imaging, July 2018, vol. 37, no 7, pp. 1678-1689. [ DOI : 10.1109/TMI.2018.2794918 ]

    https://hal.archives-ouvertes.fr/hal-01545548
  • 17O. Commowick, A. Istace, M. Kain, B. Laurent, F. Leray, M. Simon, S. Camarasu-Pop, P. Girard, R. Ameli, J.-C. Ferré, A. Kerbrat, T. Tourdias, F. Cervenansky, T. Glatard, J. Beaumont, S. Doyle, F. Forbes, J. Knight, A. Khademi, A. Mahbod, C. Wang, R. Mckinley, F. Wagner, J. Muschelli, E. Sweeney, E. Roura, X. Lladó, M. M. Santos, W. P. Santos, A. G. Silva-Filho, X. Tomas-Fernandez, H. Urien, I. Bloch, S. Valverde, M. Cabezas, F. J. Vera-Olmos, N. Malpica, C. R. G. Guttmann, S. Vukusic, G. Edan, M. Dojat, M. Styner, S. K. Warfield, F. Cotton, C. Barillot.

    Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure, in: Scientific Reports, September 2018, vol. 8, 13650 p. [ DOI : 10.1038/s41598-018-31911-7 ]

    https://www.hal.inserm.fr/inserm-01847873
  • 18A. Daouia, S. Girard, G. Stupfler.

    Estimation of Tail Risk based on Extreme Expectiles, in: Journal of the Royal Statistical Society: Series B, March 2018, vol. 80, no 2, pp. 263–292. [ DOI : 10.1111/rssb.12254 ]

    https://hal.archives-ouvertes.fr/hal-01142130
  • 19A. Daouia, S. Girard, G. Stupfler.

    Extreme M-quantiles as risk measures: From L1 to Lp optimization, in: Bernoulli, 2018, forthcoming.

    https://hal.inria.fr/hal-01585215
  • 20J. El Methni, L. Gardes, S. Girard.

    Kernel estimation of extreme regression risk measures, in: Electronic journal of statistics , 2018, vol. 12, no 1, pp. 359–398.

    https://hal.inria.fr/hal-01393519
  • 21M. Garbez, R. Symoneaux, É. Belin, Y. Caraglio, Y. Chéné, N. Dones, J.-B. Durand, G. Hunault, D. Relion, M. Sigogne, D. Rousseau, G. Galopin.

    Ornamental plants architectural characteristics in relation to visual sensory attributes: a new approach on the rose bush for objective evaluation of the visual quality, in: European Journal of Horticultural Science, 2018, vol. 83, no 3, pp. 187-201. [ DOI : 10.17660/eJHS.2018/83.3.8 ]

    https://hal.archives-ouvertes.fr/hal-01831318
  • 22S. Girard.

    Transformation of a copula using the associated co-copula, in: Dependence Modeling, 2018, vol. 6, pp. 298-308.

    https://hal.inria.fr/hal-01881969
  • 23E. Perthame, F. Forbes, A. Deleforge.

    Inverse regression approach to robust nonlinear high-to-low dimensional mapping, in: Journal of Multivariate Analysis, January 2018, vol. 163, pp. 1 - 14. [ DOI : 10.1016/j.jmva.2017.09.009 ]

    https://hal.inria.fr/hal-01347455

Invited Conferences

  • 24J. Arbel.

    A Bayesian Nonparametric Approach to Ecological Risk Assessment, in: SMPGD 2018 - Workshop on Statistical Methods for Post Genomic Data, Montpellier, France, January 2018.

    https://hal.archives-ouvertes.fr/hal-01950669
  • 25J. Arbel.

    An introduction to Bayesian nonparametric statistics, in: 2018 - Journées statistiques de Rochebrune, Megève, France, March 2018.

    https://hal.archives-ouvertes.fr/hal-01950668
  • 26J. Arbel.

    Bayesian neural network priors at the level of units, in: CMStat 2018 - 11th International Conference of Computational and Methodological Statistics, Pisa, Italy, December 2018.

    https://hal.archives-ouvertes.fr/hal-01950661
  • 27J. Arbel.

    Some distributional properties of Bayesian neural networks, in: Workshop on Bayesian nonparametrics, Bordeaux, France, July 2018.

    https://hal.archives-ouvertes.fr/hal-01950667
  • 28F. Forbes, A. Arnaud, R. Steele, B. Lemasson, E. L. Barbier.

    Bayesian mixtures of multiple scale distributions, in: CMStatistics 2018 - 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy, December 2018.

    https://hal.archives-ouvertes.fr/hal-01941682
  • 29F. Forbes, H. Lu, J. Arbel.

    Non parametric Bayesian priors for hidden Markov random fields, in: JSM 2018 - Joint Statistical Meeting, Vancouver, Canada, JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association, July 2018, pp. 1-38.

    https://hal.archives-ouvertes.fr/hal-01941679
  • 30F. Forbes, H. Lu, J. Arbel.

    Non parametric Bayesian priors for hidden Markov random fields: application to image segmentation, in: BNPSI 2018 : Workshop on Bayesian non parametrics for signal and image processing, Bordeaux, France, July 2018.

    https://hal.archives-ouvertes.fr/hal-01941687
  • 31S. Girard.

    Estimation of extreme regression risk measures, in: 2018 - Workshop Rare Events, Extremes and Machine Learning, Paris, France, May 2018.

    https://hal.inria.fr/hal-01800772
  • 32T. Rahier, S. Marié, S. Girard, F. Forbes.

    Screening strong pairwise relationships for fast Bayesian network structure learning, in: 2nd Italian-French Statistics Seminar - IFSS, Grenoble, France, September 2018.

    https://hal.archives-ouvertes.fr/hal-01941685
  • 33G. Stupfler, S. Girard.

    Estimation of high-dimensional extreme conditional expectiles, in: CMStatistics 2018 - 11th International Conference of the ERCIM WG on Computing and Statistics, Pisa, Italy, December 2018.

    https://hal.archives-ouvertes.fr/hal-01942210

International Conferences with Proceedings

  • 34R. Azaïs, J.-B. Durand, C. Godin.

    Approximation of trees by self-nested trees, in: ALENEX 2019 - Algorithm Engineering and Experiments, San Diego, United States, January 2019, pp. 1-24, https://arxiv.org/abs/1810.10860. [ DOI : 10.10860 ]

    https://hal.archives-ouvertes.fr/hal-01294013
  • 35F. Boux, F. Forbes, J. Arbel, E. L. Barbier.

    Dictionary-Free MR Fingerprinting Parameter Estimation Via Inverse Regression, in: Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, France, Proceedings of Joint Annual Meeting ISMRM-ESMRMB 2018, June 2018, pp. 1-2.

    https://hal.archives-ouvertes.fr/hal-01941630
  • 36V. Muñoz Ramírez, F. Forbes, J. Arbel, A. Arnaud, M. Dojat.

    Quantitative MRI characterization of brain abnormalities in de novo Parkinsonian patients, in: IEEE International Symposium on Biomedical Imaging, Venice, Italy, Proceedings of IEEE International Symposium on Biomedical Imaging, April 2019.

    https://hal.archives-ouvertes.fr/hal-01970682
  • 37P.-A. Rodesch, V. Rebuffel, C. Fournier, F. Forbes, L. Verger.

    Spectral CT reconstruction with an explicit photon-counting detector model: a " one-step " approach, in: SPIE Medical Imaging, Houston, United States, February 2018, vol. 10573, pp. 1-4. [ DOI : 10.1117/12.2285792 ]

    https://hal.inria.fr/hal-01652017

National Conferences with Proceedings

  • 38C. Albert, A. Dutfoy, L. Gardes, S. Girard.

    Un nouvel estimateur des quantiles extrêmes basé sur le modèle “log Weibull-tail” généralisé, in: SFDS 2018 - 50èmes Journées de Statistique, Saclay, France, May 2018, pp. 1-6.

    https://hal.inria.fr/hal-01807672
  • 39K. Ashurbekova, S. Achard, F. Forbes.

    Robust structure learning using multivariate T-distributions, in: 50e Journées de la Statistique de la SFdS, Saclay, France, May 2018, pp. 1-6.

    https://hal.archives-ouvertes.fr/hal-01941643
  • 40H. Lu, J. Arbel, F. Forbes.

    Bayesian Nonparametric Priors for Hidden Markov Random Fields, in: 50e Journées de la Statistique de la SFdS, Saclay, France, Actes 50e Journées de la Statistique de la SFdS, May 2018, pp. 1-5.

    https://hal.archives-ouvertes.fr/hal-01941638
  • 41T. Rahier, S. Marié, S. Girard, F. Forbes.

    Fast Bayesian Network Structure Learning using Quasi-Determinism Screening, in: 9èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, Toulouse, France, Actes des 9èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, June 2018.

    https://hal.archives-ouvertes.fr/hal-01941683

Conferences without Proceedings

  • 42C. Albert, A. Dutfoy, S. Girard.

    Extrapolation limits of extreme-value methods for return-levels estimation, in: EGU General Assembly, Vienna, Austria, April 2018.

    https://hal.inria.fr/hal-01773660
  • 43J. El Methni, L. Gardes, S. Girard.

    Kernel estimation of extreme regression risk measures, in: ICOR 2018 - 13th International Conference on Operations Research, La Havane, Cuba, March 2018, 1 p.

    https://hal.archives-ouvertes.fr/hal-01745322
  • 44M. Vladimirova, J. Arbel, P. Mesejo.

    Bayesian neural network priors at the level of units, in: AABI 2018 - 1st Symposium on Advances in Approximate Bayesian Inference, Montréal, Canada, December 2018, pp. 1-6.

    https://hal.archives-ouvertes.fr/hal-01950659
  • 45M. Vladimirova, J. Arbel, P. Mesejo.

    Bayesian neural networks become heavier-tailed with depth, in: NeurIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, December 2018, pp. 1-7.

    https://hal.archives-ouvertes.fr/hal-01950658
  • 46F. Zheng, M. Jalbert, F. Forbes, S. Bonnet, A. Wojtusciszyn, S. Lablanche, P.-Y. Benhamou.

    Caractérisation de la variabilité glycémique journalière chez le patient avec diabète de type 1, in: SFD 2018 - Congrès annuel de la Société Francophone du Diabète, Marseille, France, March 2019.

    https://hal.archives-ouvertes.fr/hal-01971621

Scientific Books (or Scientific Book chapters)

  • 47J. Arbel.

    Clustering Milky Way's Globulars: a Bayesian Nonparametric Approach, in: Statistics for Astrophysics: Bayesian Methodology, 2018, pp. 113-137.

    https://hal.archives-ouvertes.fr/hal-01950656
  • 48M. Clausel, J.-B. Durand.

    Modèles génératifs, in: Data Science. Cours et exercices, Eyrolles, August 2018.

    https://hal.inria.fr/hal-01961722
  • 49F. Forbes.

    Mixture Models for Image Analysis, in: Handbook of Mixture Analysis, CRC press, December 2018.

    https://hal.archives-ouvertes.fr/hal-01970681
  • 50C. Maggia, T. Mistral, S. Doyle, F. Forbes, A. Krainik, D. Galanaud, E. Schmitt, S. Kremer, I. Troprès, E. L. Barbier, J.-F. Payen, M. Dojat.

    Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes (MICCAI), ed Crimi Aea (Springer International Publishing AG), 2018.

    https://hal.archives-ouvertes.fr/hal-01704679
  • 51K. K. Mengersen, E. Duncan, J. Arbel, C. Alston-Knox, N. White.

    Applications in Industry, in: Handbook of mixture analysis, CRC press, 2018.

    https://hal.archives-ouvertes.fr/hal-01963798

Other Publications

References in notes
  • 75C. 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
  • 76P. Embrechts, C. Klüppelberg, T. Mikosh.

    Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
  • 77F. Ferraty, P. Vieu.

    Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
  • 78S. 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.
  • 79K. Li.

    Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, pp. 316–327.
  • 80B. Olivier, J.-B. Durand, A. Guérin-Dugué, M. Clausel.

    Eye-tracking data analysis using hidden semi-Markovian models to identify and characterize reading strategies, in: 19th European Conference on Eye Movements (ECM 2017), Wuppertal, Germany, August 2017.

    https://hal.inria.fr/hal-01671224


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