Catherine Matias
Catherine Matias
CNRS, Université Pierre et Marie Curie, COSTNET CA15109
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Identifiability of parameters in latent structure models with many observed variables
ES Allman, C Matias, JA Rhodes
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
Asymptotics of the maximum likelihood estimator for general hidden Markov models
R Douc, C Matias
Bernoulli, 381-420, 2001
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
Modeling heterogeneity in random graphs through latent space models: a selective review
C Matias, S Robin
ESAIM: Proceedings and Surveys 47, 55-74, 2014
A semiparametric extension of the stochastic block model for longitudinal networks
C Matias, T Rebafka, F Villers
Biometrika 105 (3), 665-680, 2018
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
Inferring sparse Gaussian graphical models with latent structure
C Ambroise, J Chiquet, C Matias
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
Simone: Statistical inference for modular networks
J Chiquet, A Smith, G Grasseau, C Matias, C Ambroise
Bioinformatics 25 (3), 417-418, 2009
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
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
Convergence of the groups posterior distribution in latent or stochastic block models
M Mariadassou, C Matias
Semiparametric deconvolution with unknown noise variance
C Matias
ESAIM: Probability and Statistics 6, 271-292, 2002
Network motifs: mean and variance for the count
C Matias, S Schbath, E Birmelé, JJ Daudin, S Robin
REVSTAT-Statistical Journal 4 (1), 31–51-31–51, 2006
Adaptivity in convolution models with partially known noise distribution
C Butucea, C Matias, C Pouet
Nine quick tips for analyzing network data
V Miele, C Matias, S Robin, S Dray
PLOS Computational Biology 15 (12), e1007434, 2019
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
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
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
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