Team MISTIS

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Bibliography

Major publications by the team in recent years

[1]
C. 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.
[2]
G. 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.
[3]
G. 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.
[4]
B. Chalmond, S. Girard.
Nonlinear modeling of scattered multivariate data and its application to shape change, in: IEEE Trans. PAMI, 1999, vol. 21(5), p. 422–432.
[5]
F. Forbes, N. Peyrard.
Hidden Markov Random Field Model Selection Criteria based on Mean Field-like Approximations, in: in IEEE trans. PAMI, August 2003, vol. 25(9), p. 1089–1101.
[6]
F. Forbes, A. E. Raftery.
Bayesian Morphology: Fast Unsupervised Bayesian Image analysis, in: Journal of the American Statistical Association, 1999, vol. 94, no 446, p. 555-568.
[7]
G. Fort, E. Moulines.
Convergence of the Monte-Carlo EM for curved exponential families, in: Annals of Statistics, 2003, vol. 31, no 4, p. 1220-1259.
[8]
L. Gardes, S. Girard.
Estimating extreme quantiles of Weibull tail-distributions, in: Communication in Statistics - Theory and Methods, 2005, vol. 34, p. 1065–1080.
[9]
S. Girard.
A nonlinear PCA based on manifold approximation, in: Computational Statistics, 2000, vol. 15(2), p. 145-167.
[10]
S. Girard, S. Iovleff.
Auto-Associative Models and Generalized Principal Component Analysis, in: Journal of Multivariate Analysis, 2005, vol. 93, no 1, p. 21–39.

Publications of the year

Doctoral dissertations and Habilitation theses

[11]
C. Bouveyron.
Modélisation et classification des données de grande dimension. Application à l'analyse d'images, Ph. D. Thesis, Université Grenoble 1, septembre 2006
http://tel.archives-ouvertes.fr/tel-00109047.

Articles in refereed journals and book chapters

[12]
C. Bouveyron, S. Girard, C. Schmid.
Class-specific subspace discriminant analysis for high-dimensional data, in: Lecture Notes in Computer Science, Berlin Heidelberg, C. Saunder (editor), Springer-Verlag, 2006, vol. 3940, p. 139–150.
[13]
C. Bouveyron, S. Girard, C. Schmid.
High Dimensional Discriminant analysis, in: Communications in Statistics, to appear, 2006.
[14]
G. Celeux, F. Forbes, C. Robert, M. Titterington.
Deviance Information Criteria for missing data models. With discussion, in: Bayesian Analysis, to appear, 2006.
[15]
C. Chen, F. Forbes, O. Francois.
FASTRUCT: Model-based clustering made faster, in: Molecular Ecology Notes, to appear, 2006.
[16]
J. Diebolt, L. Gardes, S. Girard, A. Guillou.
Bias-reduced estimators of the Weibull tail-coefficient, in: Test, to appear, 2006.
[17]
J. Diebolt, M. Garrido, S. Girard.
A Goodness-of-fit Test for the Distribution Tail, in: Topics in extreme values, New-York, M. Ahsanullah, S. Kirmani (editors), to appear, Nova Science, 2006.
[18]
F. Forbes, G. Fort.
Combining Monte Carlo and Mean field like methods for inference in hidden Markov Random Fields, in: IEEE trans. on Image Processing, to appear, 2006.
[19]
F. Forbes, N. Peyrard, C. Fraley, D. Georgian-Smith, D. Goldhaber, A. Raftery.
Model-Based Region-of-Interest Selection in Dynamic Breast MRI, in: Journal of Computer Assisted Tomography, July/August 2006, vol. 30, no 4, p. 675-687.
[20]
L. Gardes, S. Girard.
Asymptotic properties of a Pickands type estimator of the extreme value index, in: Focus on probability theory, New-York, L. R. Velle (editor), Nova Science, 2006, p. 133–149.
[21]
L. Gardes, S. Girard.
Comparison of Weibull tail-coefficient estimators, in: REVSTAT - Statistical Journal, 2006, vol. 4, no 2, p. 373–188.
[22]
J. Geffroy, S. Girard, P. Jacob.
Asymptotic normality of the L1 -error of a boundary estimator, in: Nonparametric Statistics, 2006, vol. 18, no 1, p. 21–31.

Publications in Conferences and Workshops

[23]
C. Amblard, S. Girard.
A semiparametric family of bivariate copulas: dependence properties and estimation procedures, in: IMS Annual Meeting and X Brazilian School of Probability, Rio de Janeiro, Brésil, juillet 2006.
[24]
J. Blanchet, C. Bouveyron.
Modèle markovien caché pour la classification supervisée de données de grande dimension spatialement corrélées, in: 38èmes Journées de Statistique de la Société Française de Statistique, Clamart, France, Mai 2006.
[25]
J. Blanchet, F. Forbes.
Triplet Markov fields designed for supervised classification of textured images, in: COMPSTAT, 17th symposium of the IASC, Roma, Italy, 2006.
[26]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional data clustering, in: COMPSTAT, 17th symposium of the IASC, Roma, Italy, August 2006.
[27]
C. Bouveyron, J. Kannala, C. Schmid, S. Girard.
Object localization by subspace clustering of local descriptors, in: 5th Indian Conference on Computer Vision, Graphics and Image Processing, Madurai, Inde, décembre 2006.
[28]
G. Dewaele, F. Devernay, R. Horaud, F. Forbes.
The alignment between 3D-data and articulated shapes with bending surfaces, European Conf. Computer Vision, 2006.
[29]
S. Girard, A. Iouditski, A. Nazin.
On optimal and adaptive non-parametric estimation for periodic frontier via linear programming, in: Third International Control Conference, Moscou, Russia, juin 2006.
[30]
S. Girard, S. Iovleff.
Auto-Associative models and generalized Principal Component Analysis, in: Workshop principal manifolds for data cartography and dimension reduction, Leicester, UK, aout 2006.
[31]
S. Girard, L. Menneteau.
Estimation of star-shaped supports via smoothed extreme value estimators of non-uniform point processes boundaries, in: IMS Annual Meeting and X Brazilian School of Probability, Rio de Janeiro, Brésil, juillet 2006.
[32]
B. Schrerrer, M. Dojat, F. Forbes, C. Garbay.
Distributed and Cooperative Markovian Segmentation of tissues and structures in MRI brain scans, in: HBM meeting, Florence Italy, 2006.
[33]
B. Schrerrer, M. Dojat, F. Forbes, C. Garbay.
Segmentation Markovienne distribuée et coopérative des tissus et des structures présents dans des IRM cérébrales, in: RFIA, Tours, France, 2006.
[34]
M. Vignes, F. Forbes.
A statistical glance at clustering models to fit biological network and expression data, in: 31st conference on Stochastic Processes and their Applications, Paris, France, 2006.
[35]
M. Vignes, F. Forbes.
Integrated Markov models for clustering gene expression data, in: Journées MAS de la SMAI, Lille, France, 2006.
[36]
M. Vignes, F. Forbes.
Integrated Markov models for clustering genes combining individual features and pairwise relationships, in: 4th workshop on Statistical methods for post-genomic data, Toulouse, France, 2006.
[37]
M. Vignes, F. Forbes.
Markov Random Fields for clustering genes, in: 2eme Recontres Inter-Associations: la classification et ses applications, Lyon, France, 2006.

References in notes

[38]
C. Amblard, S. Girard.
Symmetry and dependence properties within a semiparametric family of bivariate copulas, in: Nonparametric Statistics, 2002, vol. 14, no 6, p. 715–727.
[39]
D. Benboudjema, W. Pieczynski.
Unsupervised image segmentation using triplet Markov fields, in: Computer Vision and Image Understanding, 2005, vol. 99, no 3, p. 476–498.
[40]
G. Bouchard, S. Girard, A. Iouditski, A. Nazin.
Nonparametric Frontier estimation by linear programming, in: Automation and Remote Control, 2004, vol. 65, no 1, p. 58–64.
[41]
G. Bouchard, S. Girard, A. Iouditski, A. Nazin.
Some Linear programming methods for frontier estimation, in: Applied Stochastic Models in Business and Industry, 2005, vol. 21, no 2, p. 175–185.
[42]
C. Bouveyron, S. Girard, C. Schmid.
Une nouvelle méthode de classification pour la reconnaissance de formes, in: 20e colloque GRETSI sur le traitement du signal et des images, Louvain-la-Neuve, Belgium, September 2005.
[43]
J. Diebolt, M. El-Aroui, M. Garrido, S. Girard.
Quasi-conjugate Bayes estimates for GPD parameters and application to heavy tails modelling, in: Extremes, 2005, vol. 8, p. 57–78.
[44]
J. Diebolt, S. Girard.
A Note on the asymptotic normality of the ET method for extreme quantile estimation, in: Statistics and Probability Letters, 2003, vol. 62, no 4, p. 397–406.
[45]
G. Dorkó, C. Schmid.
Object Class Recognition Using Discriminative Local Features, Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence, updated 13 September, 2005.
[46]
P. Embrechts, C. Klüppelberg, T. Mikosh.
Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
[47]
F. Ferraty, P. Vieu.
Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
[48]
L. Gardes.
Estimation d'une fonction quantile extrême, Ph. D. Thesis, Université Montpellier 2, october 2003.
[49]
L. Gardes, S. Girard.
Estimating extreme quantiles of Weibull-tail distributions, in: STATDEP, Statistics for dependent data, Paris-Malakoff, janvier 2005.
[50]
L. Gardes, S. Girard.
Statistical Inference for Weibull-tail distributions, in: Workshop on risk analysis and extreme values, Paris, juin 2005.
[51]
S. Girard.
A nonlinear PCA based on manifold approximation, in: Computational Statistics, 2000, vol. 15(2), p. 145–167.
[52]
S. 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.
[53]
S. Girard.
On the asymptotic normality of the L1 - error for Haar series estimates of Poisson point processes boundaries, in: Statistics and Probability Letters, 2004, vol. 66, p. 81–90.
[54]
S. Girard, A. Iouditski, A. Nazin.
L1 -Optimal Nonparametric Frontier Estimation via Linear Programming, in: Automation and Remote Control, 2005, vol. 66, no 12, p. 2000–2018.
[55]
S. 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.
[56]
S. Girard, P. Jacob.
Projection estimates of point processes boundaries, in: Journal of Statistical Planning and Inference, 2003, vol. 116, no 1, p. 1–15.
[57]
S. Girard, P. Jacob.
Extreme values and kernel estimates of point processes boundaries, in: ESAIM: Probability and Statistics, 2004, vol. 8, p. 150–168.
[58]
S. Girard, L. Menneteau.
Central limit theorems for smoothed extreme value estimates of point processes boundaries, in: Journal of Statistical Planning and Inference, 2005, vol. 135, no 2, p. 433-460.
[59]
T. Hastie, R. Tibshirani, J. Friedman.
The Elements of Statistical Learning, Springer, NewYork, 2001.
[60]
K. LI.
Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, p. 316–342.
[61]
D. Lowe.
Distinctive image features from scale-invariant keypoints, in: International Journal of Computer Vision, 2004, vol. 60, no 2, p. 91-110.
[62]
R. B. Nelsen.
An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.
[63]
J. Pritchard, M. Stephens, P. Donnelly.
Inference of Population Structure Using Multilocus Genotype Data, in: Genetics, 2000, vol. 155, p. 945–959.

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