| Identifiability of parameters in latent structure models with many observed variables ES Allman, C Matias, JA Rhodes | 673 | 2009 |
| 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 | 360 | 2017 |
| 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 | 195 | 2020 |
| Asymptotics of the maximum likelihood estimator for general hidden Markov models R Douc, C Matias | 160 | 2001 |
| Modeling heterogeneity in random graphs through latent space models: a selective review C Matias, S Robin ESAIM: Proceedings and Surveys 47, 55-74, 2014 | 111 | 2014 |
| A semiparametric extension of the stochastic block model for longitudinal networks C Matias, T Rebafka, F Villers Biometrika 105 (3), 665-680, 2018 | 99 | 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 | 90 | 2005 |
| Inferring sparse Gaussian graphical models with latent structure C Ambroise, J Chiquet, C Matias | 78 | 2009 |
| 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 | 67 | 2012 |
| Simone: Statistical inference for modular networks J Chiquet, A Smith, G Grasseau, C Matias, C Ambroise Bioinformatics 25 (3), 417-418, 2009 | 64 | 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 | 62 | 2011 |
| Convergence of the groups posterior distribution in latent or stochastic block models M Mariadassou, C Matias | 49 | 2015 |
| 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 | 47 | 2015 |
| Semiparametric deconvolution with unknown noise variance C Matias ESAIM: Probability and Statistics 6, 271-292, 2002 | 38 | 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 | 34 | 2006 |
| Nine quick tips for analyzing network data V Miele, C Matias, S Robin, S Dray PLOS Computational Biology 15 (12), e1007434, 2019 | 30 | 2019 |
| Adaptivity in convolution models with partially known noise distribution C Butucea, C Matias, C Pouet | 29 | 2008 |
| Properties of the stochastic approximation EM algorithm with mini-batch sampling E Kuhn, C Matias, T Rebafka Statistics and Computing 30 (6), 1725-1739, 2020 | 28 | 2020 |
| 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 | 26 | 2014 |
| Revealing the hidden structure of dynamic ecological networks V Miele, C Matias Royal Society open science 4 (6), 170251, 2017 | 25 | 2017 |
Christophe AmbroiseProfessor of Statistics, Université d'Evry Val d'EssonneПотвърден имейл адрес: univ-evry.fr