Project Team Mistis

Members
Overall Objectives
Scientific Foundations
Software
New Results
Partnerships and Cooperations
Dissemination
Bibliography
PDF e-pub XML


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]
J. 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.
[3]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, p. 502–519.
[4]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, no 14.
[5]
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.
[6]
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.
[7]
F. 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.
[8]
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.
[9]
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.
[10]
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.
Publications of the year

Doctoral Dissertations and Habilitation Theses

[11]
L. Azizi.
Champs aléatoires de Markov cachés pour la cartographie du risque en épidémiologie, Université Joseph-Fourier - Grenoble I, December 2011.

Articles in International Peer-Reviewed Journal

[12]
C. Bouveyron, G. Celeux, S. Girard.
Intrinsic Dimension Estimation by Maximum Likelihood in Isotropic Probabilistic PCA, in: Pattern Recognition Letters, 2011, vol. 32, p. 1706-1713. [ DOI : 10.1016/j.patrec.2011.07.017 ]
http://hal.inria.fr/hal-00440372/en
[13]
J. Carreau, S. Girard.
Spatial extreme quantile estimation using a weighted log-likelihood approach, in: Journal de la Société Française de Statistique, 2011, vol. 152, no 3, p. 66–83.
[14]
A. Daouia, L. Gardes, S. Girard, A. Lekina.
Kernel estimators of extreme level curves, in: Test, 2011, vol. 20, no 14, p. 311–333.
[15]
F. Forbes, B. Scherrer, M. Dojat.
Bayesian Markov model for cooperative clustering: application to robust MRI brain scan segmentation, in: Journal de la Societe Française de Statistique, 2011, vol. 152, no 3.
[16]
L. Gardes, S. Girard, A. Guillou.
Weibull tail-distributions revisited: a new look at some tail estimators, in: Journal of Statistical Planning and Inference, 2011, vol. 141, no 1, p. 429–444.
[17]
R. 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
[18]
S. 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, 2011, vol. 7, no 5, to appear.
[19]
V. Khalidov, F. Forbes, R. Horaud.
Conjugate Mixture Models for Clustering Multimodal Data, in: Neural Computation, February 2011, vol. 23, no 2, p. 517–557. [ DOI : 10.1162/NECO_a_00074 ]
http://hal.inria.fr/inria-00590267/en
[20]
M. 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.

International Conferences with Proceedings

[21]
Best Paper
X. Alameda-Pineda, V. Khalidov, R. Horaud, F. Forbes.
Finding Audio-Visual Events in Informal Social Gatherings, in: IEEE/ACM International Conference on Multimodal Interfaces, Alicante, Spain, November 2011.
http://hal.inria.fr/inria-00623489/en
[22]
L. Amate, F. Forbes, J. Fontecave-Jallon, B. Vettier, C. Garbay.
Probabilistic Model Definition for Physiological State Monitoring, in: IEEE International Workshop on Statistical Signal Processing 2011 (SSP11), June 28-30 2011.
[23]
A. Asenov, Y. Courant, G. Ducharme, V. Gerousis, S. Girard.
How can statistics methods help to address variability issues?, in: 2nd European Workshop on CMOS Variability, Grenoble, 2011.
[24]
L. Azizi, D. Abrial, M. Charras-Garrido, F. Forbes.
Risk mapping based on hidden Markov random field and variational approximations, in: 1st Conference on Spatial Statistics 2011 - Mapping Global Change, University of Twente, Enschede, The Netherlands, 2011.
[25]
L. Azizi, F. Forbes, S. Doyle, M. Charras-Garrido, D. Abrial.
Spatio-temporal Markov Random Field approach to risk mapping, in: The 14th Applied Stochastic Models and Data Analysis (ASMDA2011) conference of the ASMDA International Society, Rome, Italy, June 2011.
[26]
L. Chaari, F. Forbes, P. Ciuciu, T. Vincent, M. Dojat.
Bayesian Variational Approximation for the Joint Detection Estimation of Brain Activity in fMRI, in: IEEE International Workshop on Statistical Signal Processing 2011 (SSP11), June 28-30 2011.
[27]
L. Chaari, F. Forbes, T. Vincent, M. Dojat, P. Ciuciu.
Med Image Comput Comput Assist Interv, 2011, vol. 14, no Pt 2, p. 260-8.
http://hal.inria.fr/inserm-00635384/en
[28]
M. Chavent, S. Girard, V. Kuentz, B. Liquet, T. Nguyen, J. Saracco.
An adaptive SIR method for block-wise evolving data streams, in: XIVth International Symposium of Applied Stochastic Models and Data Analysis (ASMDA 2011), Rome, Italy, 2011, 8 p.
http://hal.inria.fr/hal-00601924/en
[29]
A. Clément, S. Laurens, S. Girard.
A Novel Damage Sensitive Feature Based on State-Space Representation, in: 8th International Workshop on Strutural Health Monitoring, Stanford, USA, septembre 2011.
[30]
E. Deme, L. Gardes, S. Girard.
A new semi-parametric family of estimators for the second order parameter, in: 7th International Conference on Extreme Value Analysis, Lyon, juin 2011.
[31]
J. El-Methni, L. Gardes, S. Girard, A. Guillou.
Estimation of a new parameter discriminating between Weibull tail-distributions and heavy-tailed distributions, in: 7th International Conference on Extreme Value Analysis, Lyon, juin 2011.
[32]
L. Gardes, S. Girard.
Functional kernel estimators of conditional extreme quantiles, in: 2nd International Workshop on Functional and Operatorial Statistics, Santander, Spain, 2011.
[33]
S. Girard, A. Guillou, G. Stupfler.
Estimating an endpoint using high order moments, in: 7th International Conference on Extreme Value Analysis, Lyon, juin 2011.
[34]
S. Girard, L. Menneteau.
Strong invariance principles for tail quantile processes with applications to extreme value index estimation, in: 7th International Conference on Extreme Value Analysis, Lyon, juin 2011.
[35]
K. Qin, F. Forbes.
Dynamic Regional Harmony Search with Opposition and Local Learning, in: Genetic and Evolutionary Computation Conference 2011 (Gecco 2011), Dublin, July 2011.
[36]
K. Qin, F. Forbes.
Harmony Search with Differential Mutation Based Pitch Adjustment, in: Genetic and Evolutionary Computation Conference 2011 (Gecco 2011), Dublin, July 2011.

National Conferences with Proceeding

[37]
L. Azizi, F. Forbes, M. Charras-Garrido, D. Abrial, S. Doyle.
Initialisation de l'algorithme EM champ-moyen pour les mélanges de Poisson pour données spatiales et application à la cartographie du risque en épidémiologie, in: 43èmes Journées de Statistique organisées par la Société Française de Statistique, Tunis, Tunisia, May 2011.
[38]
L. Chaari, F. Forbes, P. Ciuciu, T. Vincent, M. Dojat.
A Variational Bayesian approach for the Joint Detection Estimation of Brain Activity in functional MRI, in: 43èmes Journées de Statistique organisées par la Société Française de Statistique, Tunis, Tunisia, May 2011.
[39]
E. Deme, L. Gardes, S. Girard.
Estimation semi-paramétrique du paramètre de second ordre en statistique des valeurs extrêmes, in: 43èmes Journées de Statistique organisées par la Société Française de Statistique, Tunis, mai 2011.
[40]
J. El-Methni, L. Gardes, S. Girard, A. Guillou.
Estimation d'un paramètre de queue commun aux lois de type Weibull et au domaine d'attraction de Fréchet, in: 43èmes Journées de Statistique organisées par la Société Française de Statistique, Tunis, mai 2011.

Scientific Books (or Scientific Book chapters)

[41]
A. 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, 2011, to appear.
[42]
L. Gardes, S. Girard.
Functional kernel estimators of conditional extreme quantiles, in: Recent advances in functional data analysis and related topics, F. Ferraty (editor), Springer, Physica-Verlag, 2011, p. 135–140.

Internal Reports

[43]
L. Azizi, F. Forbes, S. Doyle, M. Charras-Garrido, D. Abrial.
Spatial risk mapping for rare disease with hidden Markov fields and variational EM, INRIA, March 2011, no RR-7572.
http://hal.inria.fr/inria-00577793/en

Other Publications

[44]
L. Bergé, C. Bouveyron, S. Girard.
HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data, 2011.
http://hal.archives-ouvertes.fr/hal-00541203
[45]
A. Daouia, L. Gardes, S. Girard.
On kernel smoothing for extremal quantile regression, 2011.
http://hal.inria.fr/hal-00630726/en
[46]
E. Deme, L. Gardes, S. Girard.
On the estimation of the second order parameter in extreme-value theory, 2011.
http://hal.inria.fr/hal-00634573/en
[47]
J. Durand, S. Girard, V. Ciriza, L. Donini.
Optimization of power consumption and user impact based on point process modeling of the request sequence, 2011.
http://hal.archives-ouvertes.fr/hal-00412509/fr/
[48]
J. El-Methni, L. Gardes, S. Girard, A. Guillou.
Estimation of extreme quantiles from heavy and light tailed distributions, 2011.
http://hal.inria.fr/hal-00627964/en
[49]
L. Gardes, S. Girard.
Functional kernel estimators of large conditional quantiles, 2011.
http://hal.inria.fr/hal-00608192/en
[50]
L. Gardes, A. Guillou, A. Schorgen.
Estimating the conditional tail index by integrating a kernel conditional quantile estimator, 2011.
http://hal.inria.fr/inria-00578479/en
[51]
S. Girard, A. Guillou, G. Stupfler.
Frontier estimation with kernel regression on high order moments, 2011.
http://hal.archives-ouvertes.fr/hal-00499369/fr/
References in notes
[52]
C. 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.
[53]
C. 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
[54]
M. 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
[55]
C. Chen, F. Forbes, O. Francois.
FASTRUCT: Model-based clustering made faster, in: Molecular Ecology Notes, 2006, vol. 6, p. 980–983.
[56]
G. 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.
[57]
P. Embrechts, C. Klüppelberg, T. Mikosh.
Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
[58]
F. Ferraty, P. Vieu.
Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
[59]
O. Francois, S. Ancelet, G. Guillot.
Bayesian clustering using Hidden Markov Random Fields in spatial genetics, in: Genetics, 2006, p. 805–816.
[60]
S. 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.
[61]
Y. 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.
[62]
M. 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.
[63]
K. Li.
Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, p. 316–327.
[64]
R. Nelsen.
An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag, New-York, 1999, vol. 139.
[65]
J. Pritchard, M. Stephens, P. Donnelly.
Inference of Population Structure Using Multilocus Genotype Data, in: Genetics, 2000, vol. 155, p. 945–959.