Sarah Ouadah is a statistician and lecturer at Sorbonne Université. She works at the interface between statistical modeling and life/social sciences. Part of her research is devoted to characterizing the topology of random graphs (e.g. with goodness-of-fit tests, embedding, nodes clustering) for analyzing social and ecological networks. She also develops predictive models for (discrete) biological and ecological data exhibiting dependency structures and sparsity.

Internship offer stage_m2_netcomp.pdf

Research interests

• Random graphs models
• Count data, dependencies, sparsity
• Nonparametric statistics

Application fields

• Ecological networks
• Social networks
• Genomics

Preprints

1. M. Metodiev, M. Perrot-Dockès, S. Ouadah, P. Latouche, A. E. Raftery, S. Robin. A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates. Submitted, 2024.

Papers

1. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, C. Bailly and L. Rajjou. Variable selection in sparse multivariate GLARMA models: Application to germination control by environment. Statistical Methods & Applications, 1-34, 2025 web.
2. M. Metodiev, M. Perrot-Dockès, S. Ouadah, N. J. Irons, P. Latouche, A. E. Raftery. Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. Bayesian Analysis, 1(1), 1-28, 2024 web.
3. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet. Sign-consistent estimation in a sparse Poisson model. Statistics & Probability Letters, 110107, 2024 web.
4. V. Labeyrie, S. Ouadah, C. Raimond. Social network analysis: which contributions to the analysis of agricultural systems resilience ? Agricultural Systems, 2024 web.
5. A. Porcuna-Ferrer, V. Labeyrie, S. Alvarez-Fernandez, L. Calvet-Mir, N. F. Faye, S. Ouadah, and V. Reyes-García. Seed circulation networks and farmers’ social-ecological resilience. A case study in south-eastern Senegal. Agricultural Systems, Volume 211, 2023 web.
6. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, and T. Blein. Variable selection in sparse glarma models. Statistics, 1-30, 2022 web.
7. S. Ouadah, P. Latouche, and S.Robin. Motif-based tests for bipartite networks. Electronic Journal of Statistics,16(1), 293-330, 2022 web.
8. S. Romdhane, A. Spor, J. Aubert, D. Bru, M.C Breuil, S. Hallin, A. Mounier, S. Ouadah, M. Tsiknia, and L. Philippot. Unraveling biotic interactions determining soil microbial community assembly and functioning. ISME Journal, 1(16), 296-306, 2022 web.
9. S. Ouadah, S. Robin, and P. Latouche. Degree‐based goodness‐of‐fit tests for heterogeneous random graph models: Independent and exchangeable cases. Scandinavian Journal of Statistics, 47(1), 156-181, 2020 web.
10. V. Brault, S. Ouadah, L. Sansonnet, and C. Lévy-Leduc. Nonparametric homogeneity tests for analyzing large Hi-C data matrices. Journal of Multivariate Analysis, 165, 143-165, 2018 web.
11. P. Latouche, S. Robin, and S. Ouadah. Goodness of fit of logistic regression models for random graphs. Journal of Computational and Graphical Statistics, 27(1), 98-109, 2018 web.
12. S. Ouadah. Uniform-in-bandwidth nearest-neighbor density estimation. Statistics and Probability Letters, 83(8), 1835-1843, 2013 web.
13. S. Ouadah. Uniform-in-bandwidth kernel estimation for censored data. Journal of Statistical Planning and Inference, 143(8), 1273-1284, 2013 web.
14. P. Deheuvels, S. Ouadah. Uniform-in-bandwidth functional limit laws. Journal of Theoretical Probability, 26(3), 697-721, 2013 web.
15. P. Adamic, S. Ouadah. A kernel method for modelling interval censored competing risks. South African Statistical Journal, 43(1), 1-19, 2009 web.

R Packages

1. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, and L. Sansonnet. GlarmaVarSel: Variable Selection in Sparse GLARMA Models, version 1.0, 2021. https://cran.r-project. org/web/packages/GlarmaVarSel/.
2. V. Brault, G.Cougoulat, S. Ouadah and L. Sansonnet. MuchPoint: Multiple Change Point, version 0.6.1, 2018. https://CRAN.R-project.org/package=MuChPoint.