My office

Sorbonne Université - Campus Pierre et Marie Curie
Tours 16-26, Bureau 211 (2ème étage)
Tél : 01 44 27 37 80

Address

Sorbonne Université - Campus Pierre et Marie Curie
Boite courrier 158
4 place Jussieu
75252 PARIS Cedex 05, FRANCE

Research contact



Research

EcoNet project

The webpage for the EcoNet project can be found here.

Research themes



Publications

Preprints
  1. Vincent Miele, Catherine Matias, Marc Ohlmann, Giovanni Poggiato, Stéphane Dray, Wilfried Thuiller, Quantifying the overall effect of biotic interactions on species communities along environmental gradients. Soumis, 2021 Hal preprint.

Publications in journals
  1. Suzana de Siqueira Santos, André Fujita & Catherine Matias, Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, 9(6),pagesXXX, 2021. Hal preprint, Journal link.
  2. Christophe Botella, Stéphane Dray, Catherine Matias, Vincent Miele & Wilfried Thuiller, An appraisal of graph embeddings for comparing trophic network architectures. To appear in Methods in Ecology and Evolution, 2021 Hal preprint.
  3. Estelle Kuhn, Catherine Matias & Tabea Rebafka, Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling, Statistics and Computing, 30, 1725-1739, 2020. Journal link, Hal preprint
  4. Gautreau, Bazin, Gachet, Planel, Burlot, Dubois, Perrin, Médigue, Calteau, Cruveiller, Matias, Ambroise, Rocha, Vallenet, PPanGGOLiN: depicting microbial species diversity via a Partitioned Pangenome Graph, Plos Computational Biology, 16(3): e1007732, 2020. Journal link, PPanGGOLiN Software.
  5. Léa Longepierre & Catherine Matias, Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model. Electronic Journal of Statistics, 13(2):4157-4223, 2019. Journal link, HAL preprint.
  6. Vincent Miele, Catherine Matias, Stéphane Robin & Stéphane Dray, Nine Quick Tips for Analyzing Network Data. Plos Computational Biology, 15(12):e1007434, 2019. Journal link.
  7. Catherine Matias, Tabea Rebafka & Fanny Villers, A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika, 105(3): 665-680, 2018. Journal link, Take a look at the Supplementary material (contains an important generalisation of the model), HAL preprint, R code with datasets analyses, R package ppsbm.
  8. Laura Urbini, Blerina Sinaimeri, Catherine Matias & Marie-France Sagot, Exploring the Robustness of the Parsimonious Reconciliation Method in Host-Symbiont Cophylogeny. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(3), 738-748, 2019. Journal link, Hal preprint.
    NB: This is an extended version of that conference paper.
  9. Ana Arribas-Gil & Catherine Matias, A time warping approach to multiple sequence alignment. Statistical Applications in Genetics and Molecular Biology, 16(2): 133-144, 2017. Journal link, Hal preprint. Codes available on request.
  10. 25bis.   Vincent Miele & Catherine Matias, Revealing the hidden structure of dynamic ecological networks. Royal Society Open Science, 4:170251, 2017. Journal link. NB: This is a popularization version of the previous work (Ref 25).
  11. Catherine Matias & Vincent Miele, Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4): 1119-1141, 2017. Journal link , HAL preprint, R package dynSBM. See also the erratum.
  12. Pierre Andreoletti, Dasha Loukianova & Catherine Matias, Parametric estimation of a one-dimensional ballistic random walk in a Markov environment. ESAIM Prob. & Stat.19: 605-625, 2015. pdf link, Journal link.
  13. C. Baudet, B. Donati, B. Sinaimeri, P. Crescenzi, C. Gautier, C. Matias & M-F. Sagot, Co-phylogeny reconstruction via an approximate Bayesian computation. Systematic Biology. 64(3): 416-431, 2015. Journal link, Coala Software.
  14. Mahendra Mariadassou & Catherine Matias, Groups posterior distribution in latent or stochastic block models for matrices. Bernoulli, 21(1):537-573, 2015. Journal link.
  15. Catherine Matias & Stéphane Robin, Modeling heterogeneity in random graphs through latent space models: a selective review. Esaim Proc. & Surveys, 47: 55-74, 2014. pdf link, Journal link.
  16. Mikael Falconnet, Dasha Loukianova & Catherine Matias, Asymptotic normality and efficiency of the maximum likelihood estimator for the parameter of a ballistic random walk in a random environment. Mathematical Methods of Statistics. 23(1) :1-19, 2014. pdf link, Journal link.
  17. Francis Comets, Mikael Falconnet, Oleg Loukianov, Dasha Loukianova & Catherine Matias, Maximum likelihood estimator consistency for ballistic random walk in a parametric random environment. Stochastic Processes & Applications. 124(1) : 268-288, 2014. pdf link, Journal link.
  18. Van Hanh Nguyen & Catherine Matias, On efficient estimators of the proportion of true null hypotheses in a multiple testing setup. Scandinavian Journal of Statistics, 41(4): 1167-1194, 2014. pdf link, Journal link.
  19. Van Hanh Nguyen & Catherine Matias, Nonparametric estimation of the density of the alternative hypothesis in a multiple testing setup. Application to local false discovery rate estimation. ESAIM Prob. & Stat.. 18: 584-612, 2014. pdf link, Journal link.
  20. Ana Arribas-Gil & Catherine Matias, A context dependent pair hidden Markov model for statistical alignment. Statistical Applications in Genetics and Molecular Biology, 11(1), Article 5, 2012. pdf link, Journal link.
  21. Christophe Ambroise & Catherine Matias, New consistent and asymptotically normal parameter estimates for random graph mixture models. Journal of the Royal Statistical Society: Series B, 74(1): 3-35, 2012. pdf link , Journal link
  22. Elizabeth Allman, Catherine Matias & John Rhodes, Parameters identifiability in a class of random graph mixture models. Journal of Statistical Planning and Inference, 141: 1719-1736, 2011. pdf link, Journal link.
  23. Elizabeth Allman, Catherine Matias & John Rhodes, Identifiability of parameters in latent structure models with many observed variables. Annals of Statistics, 37(6A): 3099-3132, 2009. Journal link.
  24. Julien Chiquet, Alexander Smith, Gilles Grasseau, Catherine Matias & Christophe Ambroise, SIMoNe : Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3): 417-418, 2009. Journal link, R package SIMoNe.
  25. Christophe Ambroise, Julien Chiquet & Catherine Matias, Penalized maximum likelihood inference for sparse Gaussian graphical models with hidden structure. Electronic Journal of Statistics, 3: 205-238, 2009. Journal link.
  26. Antoine Chambaz & Catherine Matias, Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime. ESAIM Probab. & Stat., 13: 38-50, 2009. Journal link.
  27. Cristina Butucea, Catherine Matias & Christophe Pouet, Adaptive goodness-of-fit testing from indirect observations. Annales de l'Institut Henri Poincaré, 45(2): 352-372, 2009. Journal link.
  28. Cristina Butucea, Catherine Matias & Christophe Pouet, Adaptivity in convolution models with partially known noise distribution. Electronic Journal of Statistics, 2: 897-915, 2008. Journal link.
  29. Ana Arribas-Gil, Élisabeth Gassiat & Catherine Matias, Parameter estimation in pair hidden Markov models. Scandinavian Journal of Statistics, 33(4): 651-671, 2006. pdf link, Journal link.
  30. Catherine Matias, Sophie Schbath, Étienne Birmelé, Jean-Jacques Daudin & Stéphane Robin, Networks motifs : mean and variance for the count. Revstat, 4(1): 31-51, 2006. Journal link.
  31. Ismaël Castillo, Céline Lévy-Leduc & Catherine Matias, Exact adaptive estimation of the shape of a periodic function with unknown period corrupted by white noise. Mathematical Methods of Statistics, 15(2): 146-175, 2006. pdf link.
  32. Cristina Butucea & Catherine Matias, Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model. Bernoulli, 11(2): 309-340, 2005. Journal link.
  33. Catherine Matias & Marie-Luce Taupin, Minimax estimation of linear functionals in the convolution model. Mathematical Methods of Statistics, 13(3): 282-328, 2004. pdf link.
  34. Catherine Matias, Semiparametric deconvolution with unknown noise variance. ESAIM Probab. & Stat., 6: 271-292, 2002. Journal link
  35. Randal Douc & Catherine Matias, Asymptotics of the maximum likelihood estimator for general hidden Markov models. Bernoulli, 7(3): 381-420, 2001. Journal link.

Unpublished manuscripts (in french)

(Some) Conference Slides


PhD and Post-Doc Students

Present
Past

Teaching

Formation via CNRS Formation Entreprises : Analyse statistique des réseaux.
Cette année, la formation aura lieu du 28 au 30 novembre 2018. Accessible sur inscription. Plus d'infos ici.

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Others links/info

Seminars

Journals / Bibliography

Others (maths related)

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