| 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 | 406 | 2009 |
| Statistical clustering of temporal networks through a dynamic stochastic block model C Matias, V Miele arXiv preprint arXiv:1506.07464, 2015 | 145 | 2015 |
| Asymptotics of the maximum likelihood estimator for general hidden Markov models R Douc, C Matias Bernoulli 7 (3), 381-420, 2001 | 133 | 2001 |
| 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 | 81 | 2005 |
| Inferring sparse Gaussian graphical models with latent structure C Ambroise, J Chiquet, C Matias Electronic Journal of Statistics 3, 205-238, 2009 | 61 | 2009 |
| Modeling heterogeneity in random graphs through latent space models: a selective review C Matias, S Robin ESAIM: Proceedings and Surveys 47, 55-74, 2014 | 58 | 2014 |
| 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 | 55 | 2012 |
| Simone: Statistical inference for modular networks J Chiquet, A Smith, G Grasseau, C Matias, C Ambroise Bioinformatics 25 (3), 417-418, 2009 | 52 | 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 | 36 | 2011 |
| Convergence of the groups posterior distribution in latent or stochastic block models M Mariadassou, C Matias Bernoulli 21 (1), 537-573, 2015 | 35 | 2015 |
| Semiparametric deconvolution with unknown noise variance C Matias ESAIM: Probability and Statistics 6, 271-292, 2002 | 31 | 2002 |
| A semiparametric extension of the stochastic block model for longitudinal networks C Matias, T Rebafka, F Villers Biometrika 105 (3), 665-680, 2018 | 30 | 2018 |
| 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 | 29 | 2015 |
| 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 | 28 | 2006 |
| Adaptivity in convolution models with partially known noise distribution C Butucea, C Matias, C Pouet Electronic Journal of Statistics 2, 897-915, 2008 | 25 | 2008 |
| 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 | 21 | 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 | 19 | 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 | 16 | 2014 |
| Asymptotic normality and efficiency of the maximum likelihood estimator for the parameter of a ballistic random walk in a random environment M Falconnet, D Loukianova, C Matias Mathematical Methods of Statistics 23 (1), 1-19, 2014 | 16 | 2014 |
| Maximum likelihood estimator consistency for recurrent random walk in a parametric random environment with finite support F Comets, M Falconnet, O Loukianov, D Loukianova Stochastic Processes and their Applications 126 (11), 3578-3604, 2016 | 15 | 2016 |
Christophe AmbroiseProfessor of Statistics, Université d'Evry Val d'EssonneVerified email at genopole.cnrs.fr
