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Catherine Matias
Catherine Matias
CNRS, Université Pierre et Marie Curie, COSTNET CA15109
Verified email at math.cnrs.fr
Title
Cited by
Cited by
Year
Identifiability of parameters in latent structure models with many observed variables
ES Allman, C Matias, JA Rhodes
The Annals of Statistics 37 (6A), 3099-3132, 2009
5242009
Statistical clustering of temporal networks through a dynamic stochastic block model
C Matias, V Miele
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2017
2342017
Asymptotics of the maximum likelihood estimator for general hidden Markov models
R Douc, C Matias
Bernoulli, 381-420, 2001
1462001
Modeling heterogeneity in random graphs through latent space models: a selective review
C Matias, S Robin
ESAIM: Proceedings and Surveys 47, 55-74, 2014
862014
Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model
C Butucea, C Matias
Bernoulli 11 (2), 309-340, 2005
812005
Inferring sparse Gaussian graphical models with latent structure
C Ambroise, J Chiquet, C Matias
Electronic Journal of Statistics 3, 205-238, 2009
672009
A semiparametric extension of the stochastic block model for longitudinal networks
C Matias, T Rebafka, F Villers
Biometrika 105 (3), 665-680, 2018
622018
Simone: Statistical inference for modular networks
J Chiquet, A Smith, G Grasseau, C Matias, C Ambroise
Bioinformatics 25 (3), 417-418, 2009
612009
PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph
G Gautreau, A Bazin, M Gachet, R Planel, L Burlot, M Dubois, A Perrin, ...
PLoS computational biology 16 (3), e1007732, 2020
602020
New consistent and asymptotically normal parameter estimates for random‐graph mixture models
C Ambroise, C Matias
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012
602012
Parameter identifiability in a class of random graph mixture models
ES Allman, C Matias, JA Rhodes
Journal of Statistical Planning and Inference 141 (5), 1719-1736, 2011
522011
Convergence of the groups posterior distribution in latent or stochastic block models
M Mariadassou, C Matias
Bernoulli 21 (1), 537-573, 2015
442015
Cophylogeny reconstruction via an approximate Bayesian computation
C Baudet, B Donati, B Sinaimeri, P Crescenzi, C Gautier, C Matias, ...
Systematic Biology 64 (3), 416-431, 2015
412015
Semiparametric deconvolution with unknown noise variance
C Matias
ESAIM: Probability and Statistics 6, 271-292, 2002
342002
Network motifs: mean and variance for the count
C Matias, S Schbath, E Birmelé, JJ Daudin, S Robin
REVSTAT-Statistical Journal 4 (1), 31-51, 2006
312006
Adaptivity in convolution models with partially known noise distribution
C Butucea, C Matias, C Pouet
Electronic Journal of Statistics 2, 897-915, 2008
272008
Maximum likelihood estimator consistency for a ballistic random walk in a parametric random environment
F Comets, M Falconnet, O Loukianov, D Loukianova, C Matias
Stochastic Processes and their Applications 124 (1), 268-288, 2014
232014
Adaptive goodness-of-fit testing from indirect observations
C Butucea, C Matias, C Pouet
Annales de l'IHP Probabilités et statistiques 45 (2), 352-372, 2009
232009
On efficient estimators of the proportion of true null hypotheses in a multiple testing setup
VH Nguyen, C Matias
Scandinavian Journal of Statistics 41 (4), 1167-1194, 2014
202014
Nine quick tips for analyzing network data
V Miele, C Matias, S Robin, S Dray
PLOS Computational Biology 15 (12), e1007434, 2019
192019
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