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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, p. 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), p. 1055–1067.
  • 3C. Bouveyron, S. Girard, C. Schmid.
    High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, p. 502–519.
  • 4C. Bouveyron, S. Girard, C. Schmid.
    High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, no 14.
  • 5G. Celeux, S. Chrétien, F. Forbes, A. Mkhadri.
    A Component-wise EM Algorithm for Mixtures, in: Journal of Computational and Graphical Statistics, 2001, vol. 10, p. 699–712.
  • 6G. Celeux, F. Forbes, N. Peyrard.
    EM procedures using mean field-like approximations for Markov model-based image segmentation, in: Pattern Recognition, 2003, vol. 36, no 1, p. 131-144.
  • 7F. Forbes, G. Fort.
    Combining Monte Carlo and Mean field like methods for inference in hidden Markov Random Fields, in: IEEE trans. PAMI, 2007, vol. 16, no 3, p. 824-837.
  • 8F. Forbes, N. Peyrard.
    Hidden Markov Random Field Model Selection Criteria based on Mean Field-like Approximations, in: IEEE trans. PAMI, August 2003, vol. 25(9), p. 1089–1101.
  • 9S. Girard.
    A Hill type estimate of the Weibull tail-coefficient, in: Communication in Statistics - Theory and Methods, 2004, vol. 33, no 2, p. 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, p. 369–384.
Publications of the year

Articles in International Peer-Reviewed Journals

  • 11L. Bergé, C. Bouveyron, S. Girard.
    HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data, in: Journal of Statistical Software, 2012, vol. 46, no 6, p. 1–29.
    http://hal.inria.fr/hal-00541203
  • 12J. Carreau, D. Ceresetti, E. Ursu, S. Anquetin, J. Creutin, L. Gardes, S. Girard, G. Molinié.
    Evaluation of classical spatial-analysis schemes of extreme rainfall, in: Natural Hazards and Earth System Sciences, 2012, vol. 12, p. 3229–3240.
  • 13L. 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, October 2012. [ DOI : 10.1109/TMI.2012.2225636 ]
    http://hal.inria.fr/inserm-00753873
  • 14M. Charras-Garrido, L. Azizi, F. Forbes, S. Doyle, N. Peyrard, D. Abrial.
    On the difficulty to clearly identify and delineate disease risk hot spots, in: International Journal of Applied Earth Observation and Geoinformation, May 2012, available on line.
  • 15A. Daouia, L. Gardes, S. Girard.
    On kernel smoothing for extremal quantile regression, in: Bernoulli, 2013, to appear.
  • 16J. Durand, S. Girard, V. Ciriza, L. Donini.
    Optimization of power consumption and user impact based on point process modeling of the request sequence, in: Journal of the Royal Statistical Society series C, 2013, to appear.
  • 17J. El Methni, L. Gardes, S. Girard, A. Guillou.
    Estimation of extreme quantiles from heavy and light tailed distributions, in: Journal of Statistical Planning and Inference, 2012, vol. 142, no 10, p. 2735-2747.
    http://hal.inria.fr/hal-00627964
  • 18L. Gardes, S. Girard.
    Functional kernel estimators of large conditional quantiles, in: Electronic Journal of Statistics, 2012, vol. 6, p. 1715-1744.
    http://hal.inria.fr/hal-00608192
  • 19S. Girard, A. Guillou, G. Stupfler.
    Estimating an endpoint with high order moments in the Weibull domain of attraction, in: Statistics and Probability Letters, December 2012, vol. 82, p. 2136-2144.
    http://hal.inria.fr/hal-00648435
  • 20S. Girard, A. Guillou, G. Stupfler.
    Estimating an endpoint with high order moments, in: Test, 2012, vol. 21, no 4, p. 697–729.
    http://hal.inria.fr/inria-00596979
  • 21S. Girard, A. Guillou, G. Stupfler.
    Frontier estimation with kernel regression on high order moments, in: Journal of Multivariate Analysis, 2013, vol. 116, p. 172–189.
  • 22C. Hatt, F. Mankessi, J.-B. Durand, F. Boudon, F. Montes, M. Lartaud, J.-L. Verdeil, O. Monteeuis.
    Characteristics of Acacia mangium shoot apical meristems in natural and in vitro conditions in relation to heteroblasty, in: Trees - Structure and Function, 2012, vol. 26, no 3, p. 1031-1044, PDF version of the authors can be published in January 2013. [ DOI : 10.1007/s00468-012-0680-0 ]
    http://hal.inria.fr/hal-00699815
  • 23S. Joshi, A. Lombardot, P. Flatresse, C. D'Agostino, A. Juge, E. Beigne, S. Girard.
    Statistical estimation of dominant physical parameters for leakage variability in 32nanometer CMOS under supply voltage variations, in: Journal of Low Power Electronics, 2012, vol. 8, p. 113–124.

International Conferences with Proceedings

  • 24C. Amblard, S. Girard, L. Menneteau.
    Algebraic properties of copulas defined from matrices, in: Workshop on Copulae in Mathematical and Quantitative Finance, Cracovie, Pologne, juillet 2012.
  • 25C. Bakhous, F. Forbes, T. Vincent, L. Chaari, M. Dojat, P. Ciuciu.
    Adaptive experimental condition selection in event-related fMRI, in: ISBI 2012 - IEEE International Symposium on Biomedical Imaging, Barcelone, Spain, IEEE, May 2012, p. 1755-1758. [ DOI : 10.1109/ISBI.2012.6235920 ]
    http://hal.inria.fr/cea-00710489
  • 26C. Bakhous, F. Forbes, T. Vincent, M. Dojat, P. Ciuciu.
    Variational variable selection to assess experimental condition relevance in event-related fMRI, in: ISBI 2013 - IEEE International Symposium on Biomedical Imaging, San Francisco, USA, IEEE, May 2013.
  • 27C. Bouveyron, M. Fauvel, S. Girard.
    Kernel discriminant analysis and clustering with parsimonious Gaussian process models, in: ICML workshop on Object, functional and structured data : towards next generation kernel-based methods, Edinburgh, United Kingdom, 2012.
    http://hal.inria.fr/hal-00707056
  • 28L. Chaari, F. Forbes, T. Vincent, P. Ciuciu.
    Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework, in: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, October 1-5 2012.
  • 29L. Chaari, F. Forbes, T. Vincent, P. Ciuciu.
    Robust voxel-wise Joint Detection Estimation of brain activity in fMRI, in: IEEE International Conference on Image Processing (ICIP), Orlando, USA, September 30-October 3 2012.
  • 30A. Daouia, L. Gardes, S. Girard.
    On kernel smoothing for extremal quantile regression, in: 5th International Conference of the ERCIM WG on computing and statistics, Oviedo, Spain, décembre 2012.
  • 31S. Joshi, A. Lombardot, M. Belleville, E. Beigne, S. Girard.
    Statistical leakage estimation in 32nm CMOS considering cells correlations, in: 11th IEEE conference on Faible Tension Faible Consommation, Paris, juin 2012.
  • 32G. Mazo, F. Forbes, S. Girard.
    Augmented cumulative distribution networks for multivariate extreme value modelling, in: 5th International Conference of the ERCIM WG on Computing and Statistics, Oviedo, Spain, December 2012.
  • 33K. Qin, F. Raimondo, F. Forbes, Y. S. Ong.
    An Improved CUDA-Based Implementation of Differential Evolution on GPU, in: Genetic and Evolutionary Computation Conference 2012 (Gecco 2012), July 12-16 2012.

National Conferences with Proceeding

  • 34C. Bakhous, F. Forbes, T. Vincent, L. Chaari, M. Dojat, P. Ciuciu.
    Sélection de variable dans un cadre bayésien de traitement de données d'IRM fonctionnelle, in: Journées de Statistique de la Société Française de Statistique (SFdS), Brussels, Belgium, May 21-25 2012.
  • 35C. Bouveyron, M. Fauvel, S. Girard.
    Processus gaussiens parcimonieux pour la classification générative de données hétérogènes, in: 44èmes Journées de Statistique de la Société Française de Statistique, Bruxelles, Belgium, 2012.
    http://hal.inria.fr/hal-00707059
  • 36L. Chaari, F. Forbes, P. Ciuciu, T. Vincent.
    Parcel-free Joint Detection-Estimation in fMRI, in: Journées de Statistique de la Société Française de Statistique (SFdS), Brussels, Belgium, May 21-25 2012.
  • 37J. El-Methni, L. Gardes, S. Girard.
    Estimation de l'espérance conditionnelle des pertes extrêmes dans le cas de lois à queues lourdes en présence d'une covariable, in: 44èmes Journées de Statistique organisées par la Société Française de Statistique, Bruxelles, Belgique, mai 2012.
  • 38S. Girard, A. Guillou, G. Stupfler.
    Estimation de point terminal dans le domaine d'attraction de Weibull par une méthode des moments d'ordre élevé, in: 44èmes Journées de Statistique organisées par la Société Française de Statistique, Bruxelles, Belgique, mai 2012.
  • 39J. Saracco, M. Chavent, B. Liquet, V. Kuentz, T. Nguyen, S. Girard.
    Régression inverse par tranches sur flux de données, in: 44èmes Journées de Statistique organisées par la Société Française de Statistique, Bruxelles, Belgique, mai 2012.

Conferences without Proceedings

  • 40L. Bergé, C. Bouveyron, S. Girard.
    HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data, in: 1ères Rencontres R, Bordeaux, France, July 2012.
    http://hal.inria.fr/hal-00717506
  • 41L. Gardes, S. Girard.
    Functional kernel estimators of conditional extreme quantiles, in: 7èmes Journées de Statistique Fonctionnelle et Opératorielle, Montpellier, juin 2012.

Scientific Books (or Scientific Book chapters)

  • 42A. Daouia, L. Gardes, S. Girard.
    Nadaraya's estimates for large quantiles and free disposal support curves, in: Exploring research frontiers in contemporary statistics and econometrics, I. V. Keilegom, P. Wilson (editors), Springer, 2012, p. 1-22.
    http://hal.inria.fr/hal-00528670

Internal Reports

  • 43J.-B. Durand, Y. Guédon.
    Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models, Inria, February 2012, no RR-7896, 43 p, Submitted to Journal of Machine Learning Research.
    http://hal.inria.fr/hal-00675223
  • 44V. Khalidov, F. Forbes, R. Horaud.
    Calibration of A Binocular-Binaural Sensor Using a Moving Audio-Visual Target, Inria, January 2012, no RR-7865, 27 p.
    http://hal.inria.fr/hal-00662306

Other Publications

References in notes
  • 52C. Biernacki, G. Celeux, G. Govaert, F. Langrognet.
    Model-Based Cluster and Discriminant Analysis with the MIXMOD Software, in: Computational Statistics and Data Analysis, 2006, vol. 51, no 2, p. 587–600.
  • 53C. 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
  • 54C. Bouveyron, S. Girard, C. Schmid.
    High-dimensional data clustering, in: Computational Statistics & Data Analysis, 2007, vol. 52, no 1, p. 502–519.
  • 55M. Charras-Garrido.
    Modélisation des événements rares et estimation des quantiles extrêmes, méthodes de sélection de modèles pour les queues de distribution, Université Grenoble 1, juin 2002.
    http://mistis.inrialpes.fr/people/girard/Fichiers/theseGarrido.pdf
  • 56C. Chen, F. Forbes, O. Francois.
    FASTRUCT: Model-based clustering made faster, in: Molecular Ecology Notes, 2006, vol. 6, p. 980–983.
  • 57G. Dewaele, F. Devernay, R. Horaud, F. Forbes.
    The alignment between 3D-data and articulated shapes with bending surfaces, in: European Conf. Computer Vision, Lecture notes in Computer Science, 2006, no 3, p. 578-591.
  • 58P. Embrechts, C. Klüppelberg, T. Mikosh.
    Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
  • 59F. Ferraty, P. Vieu.
    Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
  • 60O. Francois, S. Ancelet, G. Guillot.
    Bayesian clustering using Hidden Markov Random Fields in spatial genetics, in: Genetics, 2006, p. 805–816.
  • 61A. E. Gelfand, A. M. Schmidt, S. Banerjee, C. Sirmans.
    Nonstationary multivariate process modeling through spatially varying coregionalization, in: Test, 2004, vol. 13, p. 263-312.
    http://dx.doi.org/10.1007/BF02595775
  • 62S. 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.
  • 63Y. Goegebeur, J. Beirlant, T. de Wet.
    Kernel estimators for the second order parameter in extreme value statistics, in: Journal of Statistical Planning and Inference, 2010, vol. 140, no 9, p. 2632–2652.
  • 64M. Gomes, L. de Haan, L. Peng.
    Semi-parametric Estimation of the Second Order Parameter in Statistics of Extremes, in: Extremes, 2002, vol. 5, no 4, p. 387–414.
  • 65R. Horaud, F. Forbes, M. Yguel, G. Dewaele, J. Zhang.
    Rigid and Articulated Point Registration with Expectation Conditional Maximization, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2011, vol. 33, no 3, p. 587–602. [ DOI : 10.1109/TPAMI.2010.94 ]
    http://hal.inria.fr/inria-00590265/en
  • 66M. Jones.
    A dependent bivariate t distribution with marginals on different degrees of freedom, in: Statistics and Probability Letters, 2002, vol. 56, no 2, p. 163-170.
  • 67S. Kotz, S. Nadarajah.
    Multivariate t Distributions and their Applications, Cambridge, 2004.
  • 68K. Li.
    Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, p. 316–327.
  • 69R. Nelsen.
    An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.
  • 70J. Pritchard, M. Stephens, P. Donnelly.
    Inference of Population Structure Using Multilocus Genotype Data, in: Genetics, 2000, vol. 155, p. 945–959.
  • 71H. Rue, S. Martino, N. Chopin.
    Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion), in: Journal of the Royal Statistical Society B, 2009, vol. 71, p. 319–392.
  • 72A. M. Schmidt, M. A. Rodriguez.
    Modelling multivariate counts varying continuously in space, in: Bayesian Statistics, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, M. West (editors), Oxford University Press, 2010.
  • 73W. T. Shaw, K. T. A. Lee.
    Bivariate Student distributions with variable marginal degrees of freedom and independence, in: Journal of Multivariate Analysis, 2008, vol. 99, no 6, p. 1276-1287.
  • 74P. Taberlet, E. Coissac, M. Hajibabaei, L. Rieseberg.
    Environmental DNA, 2011, vol. 21, Molecular Ecology, special issue.
  • 75M. Vignes, J. Blanchet, D. Leroux, F. Forbes.
    SpaCEM3, a software for biological module detection when data is incomplete, high dimensional and dependent, in: Bioinformatics, 2011, vol. 27, no 6, p. 881-882.

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