Project-Team Mistis
Personnel
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
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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

  • 11M. LOPES.
    Ecological monitoring of semi-natural grasslands: statistical analysis of dense satellite image time series with high spatial resolution, Institut National Polytechnique de Toulouse, November 2017.
    https://hal.inria.fr/tel-01684474

Articles in International Peer-Reviewed Journals

  • 12M. 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, June 2017, vol. 135, pp. 132–146. [ DOI : 10.1016/j.sigpro.2017.01.005 ]
    https://hal.archives-ouvertes.fr/hal-01426385
  • 13J. Arbel, S. Favaro, B. Nipoti, Y. W. Teh.
    Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, in: Statistica Sinica, January 2017, vol. 27, pp. 839-858, https://arxiv.org/abs/1506.04915. [ DOI : 10.5705/ss.202015.0250 ]
    https://hal.archives-ouvertes.fr/hal-01203324
  • 14J. Arbel, I. Prünster.
    A moment-matching Ferguson & Klass algorithm, in: Statistics and Computing, June 2017, vol. 27, no 1, pp. 3-17, https://arxiv.org/abs/1606.02566. [ DOI : 10.1007/s11222-016-9676-8 ]
    https://hal.archives-ouvertes.fr/hal-01396587
  • 15A. 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, 2018, forthcoming.
    https://hal.archives-ouvertes.fr/hal-01545548
  • 16A. Chiancone, F. Forbes, S. Girard.
    Student Sliced Inverse Regression, in: Computational Statistics and Data Analysis, September 2017, vol. 113, pp. 441-456. [ DOI : 10.1016/j.csda.2016.08.004 ]
    https://hal.archives-ouvertes.fr/hal-01294982
  • 17A. Chiancone, S. Girard, J. Chanussot.
    Collaborative Sliced Inverse Regression, in: Communication in Statistics - Theory and Methods, 2017, vol. 46, no 12, pp. 6035–6053. [ DOI : 10.1080/03610926.2015.1116578 ]
    https://hal.inria.fr/hal-01158061
  • 18A. Daouia, S. Girard, G. Stupfler.
    Estimation of Tail Risk based on Extreme Expectiles, in: Journal of the Royal Statistical Society: Series B, 2017. [ 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, 2017, forthcoming.
    https://hal.inria.fr/hal-01585215
  • 20J.-B. Durand, A. Allard, B. Guitton, E. Van de Weg, M. Bink, E. Costes.
    Predicting Flowering Behavior and Exploring Its Genetic Determinism in an Apple Multi-family Population Based on Statistical Indices and Simplified Phenotyping, in: Frontiers in Plant Science, June 2017, vol. 8, pp. 858–872. [ DOI : 10.3389/fpls.2017.00858 ]
    https://hal.inria.fr/hal-01564977
  • 21S. Girard, G. Stupfler.
    Intriguing properties of extreme geometric quantiles, in: REVSTAT - Statistical Journal, January 2017, vol. 15, no 1, pp. 107–139.
    https://hal.inria.fr/hal-00865767
  • 22M. Lopes, M. M. Fauvel, S. Girard, D. Sheeren.
    Object-based classification of grasslands from high resolution satellite image time series using Gaussian mean map kernels, in: Remote Sensing, July 2017, vol. 9, no 7, Article 688. [ DOI : 10.3390/rs9070688 ]
    https://hal.inria.fr/hal-01424929
  • 23M. Lopes, M. Fauvel, A. Ouin, S. Girard.
    Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation, in: Remote Sensing, October 2017, vol. 9, no 10, pp. 993:1-23. [ DOI : 10.3390/rs9100993 ]
    https://hal.archives-ouvertes.fr/hal-01613722
  • 24O. Marchal, J. Arbel.
    On the sub-Gaussianity of the Beta and Dirichlet distributions, in: Electronic Communications in Probability, October 2017, vol. 22, pp. 1-14, https://arxiv.org/abs/1705.00048.
    https://hal.archives-ouvertes.fr/hal-01521300
  • 25E. 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-01652011
  • 26M. Stehlik, P. Aguirre, S. Girard, P. Jordanova, J. Kiseľák, S. Torres-Leiva, Z. Sadovsky, A. Rivera.
    On ecosystems dynamics, in: Ecological Complexity, March 2017, vol. 29, pp. 10–29. [ DOI : 10.1016/j.ecocom.2016.11.002 ]
    https://hal.inria.fr/hal-01394734

Invited Conferences

  • 27J. Arbel.
    Approximating predictive probabilities of Gibbs-type priors, in: ERCIM - 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom, December 2017.
    https://hal.archives-ouvertes.fr/hal-01667746
  • 28J. Arbel.
    Bayesian nonparametric clustering, in: School of Statistics for Astrophysics: Bayesian methodology, Autrans, France, October 2017.
    https://hal.archives-ouvertes.fr/hal-01667760
  • 29J. Arbel.
    Bayesian nonparametric mixture models and clustering, in: Workshop 'New challenges in statistics for social sciences', Venise, Italy, October 2017.
    https://hal.archives-ouvertes.fr/hal-01667755
  • 30J. Arbel.
    Probabilités de découverte d'espèces: Bayes à la rescousse de Good & Turing, in: Journées Scientifiques d'Inria, Sophia Antipolis, France, June 2017.
    https://hal.archives-ouvertes.fr/hal-01667788
  • 31S. Girard, A. Daouia, G. Stupfler.
    Extreme M-quantiles as risk measures, in: 10th International Conference of the ERCIM WG on Computing and Statistics, London, United Kingdom, December 2017.
    https://hal.archives-ouvertes.fr/hal-01667201
  • 32S. Girard, L. Gardes.
    Estimation of the functional Weibull tail-coefficient, in: 10th International Conference on Extreme Value Analysis, Delft, Netherlands, June 2017.
    https://hal.archives-ouvertes.fr/hal-01571990
  • 33S. Girard, M. Lopes, M. M. Fauvel, D. Sheeren.
    Object-based Classification of Grassland Management Practices From High Resolution Satellite Image Time Series With Gaussian Mean Map Kernels, in: 27th Annual Conference of the International Environmetrics Society, Bergame, Italy, July 2017.
    https://hal.archives-ouvertes.fr/hal-01571079
  • 34S. Girard, G. Stupfler.
    Some negative results on extreme multivariate quantiles defined through convex optimisation, in: 10th International Conference of the ERCIM WG on Computing and Statistics, London, United Kingdom, December 2017.
    https://hal.archives-ouvertes.fr/hal-01667186
  • 35C.-C. Tu, F. Forbes, N. Wang, B. Lemasson.
    Structured Mixture of linear mappings in high dimension, in: JSM 2017 - Joint Statistical Meeting, Baltimore, United States, July 2017.
    https://hal.inria.fr/hal-01653601

International Conferences with Proceedings

  • 36C. Albert, A. Dutfoy, S. Girard.
    On the extrapolation limits of extreme-value theory for risk management, in: MMR 2017 - 10th International Conference on Mathematical Methods in Reliability, Grenoble, France, July 2017, 5 p.
    https://hal.archives-ouvertes.fr/hal-01571099
  • 37C. Albert, A. Dutfoy, S. Girard.
    On the relative approximation error of extreme quantiles by the block maxima method, in: 10th International Conference on Extreme Value Analysis, Delft, Netherlands, June 2017.
    https://hal.archives-ouvertes.fr/hal-01571047
  • 38J. Arias, P. Ciuciu, M. Dojat, F. Forbes, A. Frau-Pascual, T. Perret, J. M. Warnking.
    PyHRF: A Python Library for the Analysis of fMRI Data Based on Local Estimation of the Hemodynamic Response Function, in: 16th Python in Science Conference (SciPy 2017), Austin, TX, United States, July 2017. [ DOI : 10.25080/shinma-7f4c6e7-006 ]
    https://hal.archives-ouvertes.fr/hal-01566457
  • 39M. Cano, J. Arias, J. A. Pérez.
    Session-Based Concurrency, Reactively, in: 37th International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE), Neuchâtel, Switzerland, A. Bouajjani, A. Silva (editors), Formal Techniques for Distributed Objects, Components, and Systems, Springer, June 2017, vol. LNCS-10321, pp. 74-91. [ DOI : 10.1007/978-3-319-60225-7_6 ]
    https://hal.archives-ouvertes.fr/hal-01566466
  • 40J.-B. Durand, A. Allard, B. GUITTON, E. Van de Weg, M. C. A. M. Bink, E. Costes.
    Genetic determinism of flowering regularity over years in an apple multi-family population, in: International Symposium on Flowering, Fruit Set and Alternate Bearing, Palermo, Italy, June 2017.
    https://hal.inria.fr/hal-01565681
  • 41J.-B. Durand.
    Challenges d'analyse de données : une formation par la pratique transversale et multidisciplinaire en science des données, in: CFIES2017 - Colloque Francophone International sur l’Enseignement de la Statistique, Grenoble, France, September 2017.
    https://hal.inria.fr/hal-01611032
  • 42B. Lemasson, N. Collomb, A. Arnaud, E. Luc Barbier, F. Forbes.
    Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data, in: ISMRM International Society for Magnetic Resonance in Medicine, Honolulu, United States, April 2017.
    https://hal.inria.fr/hal-01652033
  • 43M. Lopes, M. Fauvel, A. Ouin, S. Girard.
    Potential of Sentinel-2 and SPOT5 (Take5) time series for the estimation of grasslands biodiversity indices, in: MultiTemp 2017 - 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Bruges, Belgium, June 2017, pp. 1-4. [ DOI : 10.1109/Multi-Temp.2017.8035206 ]
    https://hal.archives-ouvertes.fr/hal-01556786
  • 44P.-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.
    https://hal.inria.fr/hal-01652017
  • 45S. N. Sylla, S. Girard, A. K. Diongue, A. Diallo, C. Sokhna.
    Hierarchical kernel applied to mixture model for the classification of binary predictors, in: 61st ISI World Statistics Congress, Marrakech, Morocco, July 2017.
    https://hal.archives-ouvertes.fr/hal-01587163
  • 46V. Watson, J.-F. Trouilhet, F. Paletou, S. Girard.
    Inference of an explanatory variable from observations in a high-dimensional space: Application to high-resolution spectra of stars, in: IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM), San Sebastian, Spain, May 2017.
    https://hal.archives-ouvertes.fr/hal-01533227

National Conferences with Proceedings

  • 47C. Albert, A. Dutfoy, S. Girard.
    Etude de l'erreur relative d'approximation des quantiles extrêmes, in: 49èmes Journées de Statistique organisées par la Société Française de Statistique, Avignon, France, May 2017.
    https://hal.archives-ouvertes.fr/hal-01533220
  • 48F. Andriatsitoaina, N. Collomb, A. Arnaud, F. Forbes, J.-P. Issartel, C. Loussouarn, E. Garcion, E. Luc Barbier, B. Lemasson.
    Suivi de l'hétérogénéité de la croissance de 4 modèles de gliomes par IRM multiparamétrique analysée par clustering, in: congrès national de l’imagerie du vivant, Paris, France, November 2017.
    https://hal.inria.fr/hal-01652029
  • 49J. Arbel, D. Fraix-Burnet, S. Girard.
    Les écoles d'astrostatistique " Statistics for Astrophysics ", in: CFIES 2017 - 5ème Colloque Francophone International sur l’Enseignement de la Statistique, Grenoble, France, September 2017.
    https://hal.inria.fr/hal-01583854
  • 50B. Lemasson, N. Collomb, A. Arnaud, F. Forbes, E. L. Barbier.
    Suivi de l'hétérogénéité de la croissance des gliomes par IRM multiparamétrique analysée par clustering, in: SFRMBM Societe Francaise de Resonance Magnetique en Biologie et Medecine, Bordeaux, France, March 2017.
    https://hal.inria.fr/hal-01652300

Conferences without Proceedings

  • 51J. Arbel.
    Bayesian nonparametric inference for discovery probabilities, in: YES VIII Workshop on Uncertainty Quantification, Eindhoven, Netherlands, January 2017.
    https://hal.archives-ouvertes.fr/hal-01667794
  • 52J. Arbel.
    Investigating predictive probabilities of Gibbs-type priors, in: Mathematical Methods of Modern Statistics, Marseille, France, July 2017.
    https://hal.archives-ouvertes.fr/hal-01667765
  • 53S. Iovleff, M. Fauvel, S. Girard, C. Preda, V. Vandewalle.
    Mixture Models with Missing data Classication of Satellite Image Time Series: QUALIMADOS: Atelier Qualité des masses de données scientiques, in: Journées Science des Données MaDICS 2017, Marseille, France, June 2017, pp. 1-60.
    https://hal.archives-ouvertes.fr/hal-01649206
  • 54M. Lopes, M. Fauvel, A. Ouin, S. Girard.
    Evaluation de la biodiversité des prairies semi-naturelles par télédétection hyperspectrale, in: SFPT‐GH 2017 - 5ème colloque scientifique du groupe thématique hyperspectral de la Société Française de Photogrammétrie et Télédétection, Brest, France, May 2017, vol. 24.
    https://hal.archives-ouvertes.fr/hal-01542063
  • 55B. 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: European Conference on Eye Movements - ECEM 2017, Wuppertal, Germany, August 2017.
    https://hal.inria.fr/hal-01671224
  • 56G. Stupfler, S. Girard, A. Guillou.
    Estimating a frontier function using a high-order moments method, in: 31st European Meeting of Statisticians, Helsinki, Finland, July 2017.
    https://hal.archives-ouvertes.fr/hal-01571126

Scientific Books (or Scientific Book chapters)

  • 57J. Arbel, I. Prünster.
    Truncation error of a superposed gamma process in a decreasing order representation, in: Bayesian Statistics in Action, R. Argiento, E. Lanzarone, I. Antoniano Villalobos, A. Mattei (editors), Bayesian Statistics in Action, January 2017, vol. 194, pp. 11–19.
    https://hal.archives-ouvertes.fr/hal-01405580
  • 58M. Fauvel, S. Girard, S. Douté, L. Gardes.
    Machine Learning Methods for the Inversion of Hyperspectral Images, in: Horizons in World Physics, A. Reimer (editor), Nova Science, 2017, vol. 290, pp. 51-77.
    https://hal.inria.fr/hal-01445638
  • 59G. Kon Kam King, J. Arbel, I. Prünster.
    A Bayesian nonparametric approach to ecological risk assessment, in: Bayesian Statistics in Action, R. Argiento, E. Lanzarone, I. Antoniano Villalobos, A. Mattei (editors), Bayesian Statistics in Action, January 2017, vol. 194, pp. 151–159.
    https://hal.archives-ouvertes.fr/hal-01405593

Other Publications

  • 60J. Arbel, J.-B. Salomond.
    Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models, June 2017, 1 p, BNP 2017 - 11th Conference on Bayesian NonParametrics, Poster.
    https://hal.archives-ouvertes.fr/hal-01667781
  • 61J. El Methni, L. Gardes, S. Girard.
    Kernel estimation of extreme regression risk measures, November 2017, working paper or preprint.
    https://hal.inria.fr/hal-01393519
  • 62D. Fraix-Burnet, C. Bouveyron, S. Girard, J. Arbel.
    Unsupervised classification in high dimension, June 2017, European Week of Astronomy and Space Science (EWASS 2017), Poster.
    https://hal.archives-ouvertes.fr/hal-01569733
  • 63B. Lemasson, N. Collomb, A. Arnaud, F. Forbes, E. L. Barbier.
    Monitoring brain tumor evolution using multiparametric MRI, April 2017, 2017 IEEE International Symposium on Biomedical Imaging, Poster.
    https://hal.inria.fr/hal-01652026
References in notes
  • 64M.-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
  • 65C. 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
  • 66P. Embrechts, C. Klüppelberg, T. Mikosh.
    Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
  • 67F. Ferraty, P. Vieu.
    Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
  • 68S. 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.
  • 69K. Li.
    Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, pp. 316–327.
  • 70G. Mazo.
    A semiparametric and location-shift copula-based mixture model, July 2016, working paper or preprint.
    https://hal.archives-ouvertes.fr/hal-01263382

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