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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. Daouia, S. Girard, G. Stupfler.

    Estimation of Tail Risk based on Extreme Expectiles, in: Journal of the Royal Statistical Society series B, 2018, vol. 80, pp. 263–292.
  • 7A. 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
  • 8F. 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.
  • 9F. 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
  • 10S. 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.
Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 13A. A. Ahmad, E. H. Deme, A. Diop, S. Girard.

    Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions, in: Dependence Modeling, 2019, vol. 7, pp. 394–417.

    https://hal.inria.fr/hal-02132976
  • 14C. Albert, A. Dutfoy, L. Gardes, S. Girard.

    An extreme quantile estimator for the log-generalized Weibull-tail model, in: Econometrics and Statistics , 2019, pp. 1-39, forthcoming. [ DOI : 10.1016/j.ecosta.2019.01.004 ]

    https://hal.inria.fr/hal-01783929
  • 15C. Albert, A. Dutfoy, S. Girard.

    Asymptotic behavior of the extrapolation error associated with the estimation of extreme quantiles, in: Extremes, 2019, forthcoming.

    https://hal.archives-ouvertes.fr/hal-01692544
  • 16J. Arbel, M. Crispino, S. Girard.

    Dependence properties and Bayesian inference for asymmetric multivariate copulas, in: Journal of Multivariate Analysis, November 2019, vol. 174, pp. 104530:1-20. [ DOI : 10.1016/j.jmva.2019.06.008 ]

    https://hal.archives-ouvertes.fr/hal-01963975
  • 17J. Arbel, P. De Blasi, I. Prünster.

    Stochastic approximations to the Pitman-Yor process, in: Bayesian Analysis, June 2019, vol. 14, no 3, pp. 753-771. [ DOI : 10.1214/18-BA1127 ]

    https://hal.archives-ouvertes.fr/hal-01950654
  • 18J. Arbel, S. Favaro.

    Approximating predictive probabilities of Gibbs-type priors, in: Sankhya A, September 2019, pp. 1-21.

    https://hal.archives-ouvertes.fr/hal-01693333
  • 19J. Arbel, O. Marchal, H. T. Nguyen.

    On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables, in: ESAIM: Probability and Statistics, December 2019.

    https://hal.archives-ouvertes.fr/hal-01998252
  • 20M. Crispino, E. Arjas, V. Vitelli, N. Barrett, A. Frigessi.

    A Bayesian Mallows Approach to Non-Transitive Pair Comparison Data: How Human are Sounds?, in: Annals of Applied Statistics, June 2019, vol. 13, no 1, pp. 492-519. [ DOI : 10.1214/18-AOAS1203 ]

    https://hal.archives-ouvertes.fr/hal-01972952
  • 21A. Daouia, S. Girard, G. Stupfler.

    Extreme M-quantiles as risk measures: From L1 to Lp optimization, in: Bernoulli, February 2019, vol. 25, no 1, pp. 264-309. [ DOI : 10.3150/17-BEJ987 ]

    https://hal.inria.fr/hal-01585215
  • 22A. Daouia, S. Girard, G. Stupfler.

    Tail expectile process and risk assessment, in: Bernoulli, 2019, pp. 1-27, forthcoming.

    https://hal.archives-ouvertes.fr/hal-01744505
  • 23L. Gardes, S. Girard, G. Stupfler.

    Beyond tail median and conditional tail expectation: extreme risk estimation using tail Lp −optimisation, in: Scandinavian Journal of Statistics, 2019, pp. 1-69, forthcoming. [ DOI : 10.1111/sjos.12433 ]

    https://hal.inria.fr/hal-01726328
  • 24M. Jalbert, F. Zheng, A. Wojtusciszyn, F. Forbes, S. Bonnet, K. Skaare, P.-Y. Benhamou, S. Lablanche.

    Glycemic variability indices can be used to diagnose islet transplantation success in type 1 diabetic patients, in: Acta Diabetologica, October 2019, pp. 1-11. [ DOI : 10.1007/s00592-019-01425-3 ]

    https://hal.archives-ouvertes.fr/hal-02328170
  • 25C. Lawless, J. Arbel.

    A simple proof of Pitman-Yor's Chinese restaurant process from its stick-breaking representation, in: Dependence Modeling, 2019, vol. 7, no 1, pp. 45-52. [ DOI : 10.1515/demo-2019-0003 ]

    https://hal.archives-ouvertes.fr/hal-01950653
  • 26Q. Liu, M. Crispino, I. Scheel, V. Vitelli, A. Frigessi.

    Model-based learning from preference data, in: Annual Reviews of Statistics and its Application, March 2019, vol. 6, no 1, pp. 329-354. [ DOI : 10.1146/annurev-statistics-031017-100213 ]

    https://hal.archives-ouvertes.fr/hal-01972948
  • 27H. D. Nguyen, F. Chamroukhi, F. Forbes.

    Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model, in: Neurocomputing, November 2019, vol. 366, pp. 208-214. [ DOI : 10.1016/j.neucom.2019.08.014 ]

    https://hal.archives-ouvertes.fr/hal-02265793
  • 28H. D. Nguyen, F. Forbes, G. J. Mclachlan.

    Mini-batch learning of exponential family finite mixture models, in: Statistics and Computing, 2019, pp. 1-40, forthcoming.

    https://hal.archives-ouvertes.fr/hal-02415068
  • 29C.-C. Tu, F. Forbes, B. Lemasson, N. Wang.

    Prediction with high dimensional regression via hierarchically structured Gaussian mixtures and latent variables, in: Journal of the Royal Statistical Society: Series C Applied Statistics, 2019, pp. 1-23. [ DOI : 10.1111/rssc.12370 ]

    https://hal.archives-ouvertes.fr/hal-02263144
  • 30F. Zheng, M. Jalbert, F. Forbes, S. Bonnet, A. Wojtusciszyn, S. Lablanche, P.-Y. Benhamou.

    Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics, in: Diabetes Technology and Therapeutics, 2019, pp. 1-17, forthcoming. [ DOI : 10.1089/dia.2019.0250 ]

    https://hal.archives-ouvertes.fr/hal-02415078

Invited Conferences

  • 31M. Crispino, S. Girard, J. Arbel.

    Dependence properties and Bayesian inference for asymmetric multivariate copulas, in: CMStatistics 2019 - 12th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom, December 2019.

    https://hal.archives-ouvertes.fr/hal-02413948
  • 32F. Forbes, A. Arnaud, B. Lemasson, E. L. Barbier.

    Bayesian mixtures of multiple scale distributions, in: 2019 - 26th Summer Working Group on Model-Based Clustering, Vienna, Austria, July 2019.

    https://hal.archives-ouvertes.fr/hal-02423638
  • 33F. Forbes, A. Arnaud, B. Lemasson, E. L. Barbier.

    Component elimination strategies to fit mixtures of multiple scale distributions, in: RSSDS 2019 - Research School on Statistics and Data Science, Melbourne, Australia, Proceedings of the Research School on Statistics and Data Science 2019, July 2019, pp. 1-15.

    https://hal.archives-ouvertes.fr/hal-02415090
  • 34F. Forbes, A. Deleforge, R. Horaud, E. Perthame.

    Robust non-linear regression approach for generalized inverse problems in a high dimensional setting, in: AIP 2019 - Applied Inverse Problem conference, Grenoble, France, July 2019.

    https://hal.archives-ouvertes.fr/hal-02415115
  • 35F. Forbes, D. Wraith.

    Robust mixture modelling using skewed multivariate distributions with variable amounts of tailweight, in: JdS 2019 - 51èmes Journées de Statistique, Nancy, France, Proceedings des 51èmes Journées de Statistique 2019, June 2019.

    https://hal.archives-ouvertes.fr/hal-02423639
  • 36S. Girard.

    Un aperçu des méthodes statistiques pour la classification et la régression en grande dimension, in: Workshop "Appréhender la grande dimension" 2019, Paris, France, June 2019.

    https://hal.inria.fr/hal-02149891
  • 37S. Girard, G. Stupfler.

    Estimation of high-dimensional extreme conditional expectiles, in: CRoNoS & MDA 2019 - Final CRoNoS meeting and 2nd workshop on Multivariate Data Analysis, Limassol, Cyprus, April 2019.

    https://hal.inria.fr/hal-02099370
  • 38A. Usseglio-Carleve, S. Girard, G. Stupfler.

    Nonparametric extreme conditional expectile estimation, in: CMStatistics 2019 - 12th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom, December 2019.

    https://hal.archives-ouvertes.fr/hal-02413682

International Conferences with Proceedings

  • 39K. Ashurbekova, S. Achard, F. Forbes.

    Structure Learning via Hadamard Product of Correlation and Partial Correlation Matrices, in: EUSIPCO 2019 - 27th European Signal Processing Conference, A Coruña, Spain, IEEE, September 2019, pp. 1-5. [ DOI : 10.23919/EUSIPCO.2019.8902948 ]

    http://hal.univ-grenoble-alpes.fr/hal-02290847
  • 40R. Azaïs, J.-B. Durand, C. Godin.

    Approximation of trees by self-nested trees, in: ALENEX 2019 - Algorithm Engineering and Experiments, San Diego, United States, SIAM, 2019, pp. 39-53, https://arxiv.org/abs/1810.10860. [ DOI : 10.1137/1.9781611975499.4 ]

    https://hal.archives-ouvertes.fr/hal-01294013
  • 41P. Bruel, S. Quinito Masnada, B. Videau, A. Legrand, J.-M. Vincent, A. Goldman.

    Autotuning under Tight Budget Constraints: A Transparent Design of Experiments Approach, in: CCGrid 2019 - International Symposium in Cluster, Cloud, and Grid Computing, Larcana, Cyprus, May 2019, pp. 1-10. [ DOI : 10.1109/CCGRID.2019.00026 ]

    https://hal.inria.fr/hal-02110868
  • 42S. Girard, G. Stupfler, A. Usseglio-Carleve.

    Nonparametric extreme conditional expectile estimation, in: EVA 2019 - 11th International Conference on Extreme Value Analysis, Zagreb, Croatia, July 2019, 1 p.

    https://hal.inria.fr/hal-02186705
  • 43V. Muñoz Ramírez, F. Forbes, J. Arbel, A. Arnaud, M. Dojat.

    Quantitative MRI characterization of brain abnormalities in de novo Parkinsonian patients, in: ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Venice, Italy, Proceedings of IEEE International Symposium on Biomedical Imaging, April 2019, pp. 1-4. [ DOI : 10.1109/ISBI.2019.8759544 ]

    https://hal.archives-ouvertes.fr/hal-01970682
  • 44V. Muñoz Ramírez, F. Forbes, P. Coupé, M. Dojat.

    No Structural Differences Are Revealed by VBM in 'de novo' Parkinsonian Patients, in: MEDINFO 2019 - 17th World Congress On Medical And Health Informatics, Lyon, France, August 2019, pp. 268-272. [ DOI : 10.3233/SHTI190225 ]

    https://hal.inria.fr/hal-02426273
  • 45M. Vladimirova, J. Verbeek, P. Mesejo, J. Arbel.

    Understanding Priors in Bayesian Neural Networks at the Unit Level, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of the 36th International Conference on Machine Learning, June 2019, vol. 97, pp. 6458-6467, https://arxiv.org/abs/1810.05193 - 10 pages, 5 figures, ICML'19 conference.

    https://hal.archives-ouvertes.fr/hal-02177151

National Conferences with Proceedings

  • 46C. Albert, A. Dutfoy, S. Girard.

    Etude de l’erreur relative d’extrapolation associée à l’estimateur de Weissman pour les quantiles extrêmes, in: JdS 2019 - 51èmes Journées de Statistique, Nancy, France, Société Française de Statistique, June 2019, pp. 1-6.

    https://hal.inria.fr/hal-02149905
  • 47J.-B. Durand.

    Compétitions d’analyse des données à l’Université Grenoble Alpes : motivations, organisation et retours d’expérience, in: CFIES 2019 - Colloque francophone international sur l'enseignement de la statistique, Strasbourg, France, September 2019, pp. 1-6.

    https://hal.inria.fr/hal-02298606
  • 48B. Olivier, A. Guérin-Dugué, J.-B. Durand.

    Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences, in: JdS 2019 - 51èmes Journées de Statistique, Vandœuvre-lès-Nancy, France, June 2019, pp. 1-7.

    https://hal.inria.fr/hal-02129122
  • 49F. Zheng, S. Bonnet, F. Forbes, M. Jalbert, S. Lablanche, P.-Y. Benhamou.

    Caractérisation de la variabilité glycémique par analyse statistique multivariée, in: GRETSI 2019 - XXVIIème Colloque francophonede traitement du signal et des images, Lille, France, Proceedings du XXVIIème Colloque francophone de traitement du signal et des images, August 2019, pp. 1-4.

    https://hal.archives-ouvertes.fr/hal-02415082
  • 50F. 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 2019 - Congrès annuel de la Société Francophone du Diabète, Marseille, France, Proceedings du Congrès annuel de la Société Francophone du Diabète, March 2019.

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

Conferences without Proceedings

  • 51K. Ashurbekova, S. Achard, F. Forbes.

    Robust penalized inference for Gaussian Scale Mixtures, in: SPARS 2019 - Workshop on Signal Processing with Adaptive Sparse Structured Representations, Toulouse, France, July 2019, pp. 1-2.

    http://hal.univ-grenoble-alpes.fr/hal-02291576
  • 53F. Boux, F. Forbes, J. Arbel, E. L. Barbier.

    Dictionary learning via regression: vascular MRI application, in: CNIV 2019 - 3e Congrès National d'Imagerie du Vivant, Paris, France, February 2019, pp. 1-12.

    https://hal.archives-ouvertes.fr/hal-02428647
  • 54F. Boux, F. Forbes, J. Arbel, E. L. Barbier.

    Estimation de paramètres IRM en grande dimension via une régression inverse, in: SFRMBM 2020 - 4e congrés de la Société Française de Résonance Magnétique en Biologie et Médecine, Strasbourg, France, March 2020, 1 p.

    https://hal.archives-ouvertes.fr/hal-02428679
  • 55A. Constantin, M. Fauvel, S. Girard, S. Iovleff, Y. Tanguy.

    Classification de Signaux Multidimensionnels Irrégulièrement Echantillonnés, in: 2019 - Journée Jeunes Chercheurs MACLEAN du GDR MADICS, Paris, France, December 2019, pp. 1-2.

    https://hal.archives-ouvertes.fr/hal-02394120
  • 56P. S. Dkengne, S. Girard, S. Ahiad.

    Estimation of the extrapolation range associated with extreme-value models: Application to the assessment of sensors reliability, in: EMS 2019 - 32nd European Meeting of Statisticians, Palerme, Italy, July 2019.

    https://hal.archives-ouvertes.fr/hal-02278051
  • 57V. Muñoz Ramírez, M. Dojat, F. Forbes.

    Mixture Models for the characterization of brain abnormalities in "de novo" Parkinsonian patients, in: CNIV 2019 - 3e Congrès National d'Imagerie du Vivant, Paris, France, February 2019, pp. 1-16.

    https://hal.inria.fr/hal-02436886
  • 58V. Muñoz Ramírez, F. Forbes, A. Arnaud, M. Dojat.

    Brain abnormalities detection in de Novo Parkinsonian patients, in: OHBM 2019 - 25th Annual Meeting of the Organization for Human Brain Mapping, Rome, Italy, June 2019, pp. 1-11.

    https://hal.archives-ouvertes.fr/hal-02415101
  • 59V. Muñoz Ramírez, F. Forbes, P. Coupé, M. Dojat.

    No structural Brain differences in 'de novo' Parkinsonian patients, in: OHBM 2019 - 25th Annual Meeting of the Organization for Human Brain Mapping, Rome, Italy, June 2019, pp. 1-5.

    https://hal.archives-ouvertes.fr/hal-02192447
  • 60S. Salhi, F. Bonnefoy, S. Girard, M. Bernier, N. Barbot, R. Siragusa, E. Perret, F. Garet.

    Enhanced THz tags authentication using multivariate statistical analysis, in: IRMMW-THz 2019 - 44th International Conference on Infrared, Millimeter, and Terahertz Waves, Paris, France, September 2019, pp. 1-2.

    https://hal.archives-ouvertes.fr/hal-02282841

Scientific Books (or Scientific Book chapters)

  • 61K. K. Mengersen, E. Duncan, J. Arbel, C. Alston-Knox, N. White.

    Applications in Industry, in: Handbook of mixture analysis, S. Fruhwirth-Schnatter, G. Celeux, C. P. Robert (editors), CRC press, January 2019, pp. 1-21.

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

Scientific Popularization

  • 62V. Muñoz Ramírez, F. Forbes, A. Arnaud, E. Moro, M. Dojat.

    Anomaly detection in the MRI data of newly diagnosed Parkinsonian patients, March 2019, 4e congrès de la Société Française de Résonance Magnétique en Biologie et Médecine - SFRMBM 2019, Poster.

    https://hal.inria.fr/hal-02436613

Other Publications

  • 63J. Arbel, R. Corradin, B. Nipoti.

    Dirichlet process mixtures under affine transformations of the data, January 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01950652
  • 64J. Arbel, O. Marchal, B. Nipoti.

    On the Hurwitz zeta function with an application to the exponential-beta distribution, December 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02400451
  • 65A. Arnaud, F. Forbes, R. Steele, B. Lemasson, E. L. Barbier.

    Bayesian mixtures of multiple scale distributions, September 2019, working paper or preprint.

    https://hal.inria.fr/hal-01953393
  • 66K. Ashurbekova, A. Usseglio-Carleve, F. Forbes, S. Achard.

    Optimal shrinkage for robust covariance matrix estimators in a small sample size setting, November 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02378034
  • 67M. Bousebata, G. Enjolras, S. Girard.

    Bayesian estimation of natural extreme risk measures. Application to agricultural insurance, June 2019, Global Challenges Science Week: International interdisciplinary days of Grenoble Alpes, Poster.

    https://hal.archives-ouvertes.fr/hal-02150604
  • 68M. Bousebata, S. Girard, G. Enjolras.

    Estimation bayésienne des mesures de risques naturels extrêmes. Application à l'assurance du risque agricole, March 2019, 1 p, Assises Nationales des Risques Naturels 2019, Poster.

    https://hal.archives-ouvertes.fr/hal-02092358
  • 69F. Boux, F. Forbes, J. Arbel, E. L. Barbier.

    Inverse regression in MR Fingerprinting: reducing dictionary size while increasing parameters accuracy, October 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02314026
  • 70A. Constantin, M. Fauvel, S. Girard, S. Iovleff.

    Classification de Signaux Multidimensionnels Irrégulièrement Échantillonnés, August 2019, GRETSI 2019 - 27e Colloque francophone de traitement du signal et des images, Poster.

    https://hal.archives-ouvertes.fr/hal-02276255
  • 71A. Constantin, M. Fauvel, S. Girard, S. Iovleff.

    Supervised classification of multidimensional and irregularly sampled signals, April 2019, 1 p, Statlearn 2019 - Workshop on Challenging problems in Statistical Learning, Poster.

    https://hal.archives-ouvertes.fr/hal-02092347
  • 72L. Gardes, S. Girard.

    On the estimation of the variability in the distribution tail, December 2019, working paper or preprint.

    https://hal.inria.fr/hal-02400320
  • 73S. Girard.

    Deux méthodes statistiques pour la classification et la régression en grande dimension, June 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02156948
  • 74S. Girard, G. Stupfler, A. Usseglio-Carleve.

    Nonparametric extreme conditional expectile estimation, May 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02114255
  • 75S. Girard, G. Stupfler, A. Usseglio-Carleve.

    An Lp−quantile methodology for tail index estimation, January 2020, working paper or preprint.

    https://hal.inria.fr/hal-02311609
  • 76B. Kugler, F. Forbes, S. Douté.

    Massive hyperspectral images analysis by inverse regression of physical models, April 2019, StatLearn 2019 Workshop on Challenging problems in Statistical Learning, Poster.

    https://hal.archives-ouvertes.fr/hal-02423640
  • 77H. Lu, J. Arbel, F. Forbes.

    Bayesian nonparametric priors for hidden Markov random fields, June 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02163046
  • 78H. Lu, F. Forbes, J. Arbel.

    Bayesian Nonparametric Priors for Graph Structured Data: Application to Image Segmentation, January 2020, Bayes Comp 2020, Poster.

    https://hal.archives-ouvertes.fr/hal-02423642
  • 79H. D. Nguyen, J. Arbel, H. Lu, F. Forbes.

    Approximate Bayesian computation via the energy statistic, December 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02399934
References in notes
  • 80C. 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
  • 81P. Embrechts, C. Klüppelberg, T. Mikosh.

    Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
  • 82F. Ferraty, P. Vieu.

    Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
  • 83S. 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.
  • 84K. Li.

    Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, pp. 316–327.
  • 85B. 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
  • 86J. 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.


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