{{:users:ouadah:photo_sarah.jpg?150|}}{{page>inc&noheader&nofooter}}
Sarah Ouadah is a statistician and senior 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.

**<color #00a2e8>Research interests</color>**
  * Random graphs models
  * Count data, dependencies, sparsity
  * Nonparametric statistics

**<color #00a2e8>Application fields</color>**
  * Ecological networks
  * Social networks
  * Genomics

**<color #00a2e8>Preprints</color>**
  -  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.

**<color #00a2e8>Papers</color>**
  - 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 [[https://link.springer.com/epdf/10.1007/s10260-025-00786-0?sharing_token=exbqe9l29ndsYEwYG3u2kve4RwlQNchNByi7wbcMAY5ciFDD2CNNfY03qbVu8Q2e4LdJhf12cpWt7gcfHf6YnWZfh4YnWaeKoA-Rl8UIN4pw4l4eXGP1PL5fOCWZoeo-5C1mZj9q8BovX4BVSogCwcRRRgt1Lf4noBUjJ604NDY%3D|web]].
  - 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 [[https://arxiv.org/pdf/2305.08952|web]].
  - M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet. Sign-consistent estimation in a sparse Poisson model. Statistics & Probability Letters, 110107, 2024 [[https://www.sciencedirect.com/science/article/pii/S0167715224000762|web]].
  - V. Labeyrie, S. Ouadah, C. Raimond. Social network analysis: which contributions to the analysis of agricultural systems resilience ? Agricultural Systems, 2024 [[https://www.sciencedirect.com/science/article/abs/pii/S0308521X23002378?dgcid=author|web]].
  - 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 [[https://www.sciencedirect.com/science/article/pii/S0308521X23001555|web]].
  - M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, and T. Blein. Variable selection in sparse glarma models. Statistics, 1-30, 2022 [[https://www.tandfonline.com/doi/abs/10.1080/02331888.2022.2090943|web]].
  - S. Ouadah, P. Latouche, and S.Robin. Motif-based tests for bipartite networks. Electronic Journal of Statistics,16(1), 293-330, 2022 [[https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-1/Motif-based-tests-for-bipartite-networks/10.1214/21-EJS1944.full|web]].
  - 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 [[https://www.nature.com/articles/s41396-021-01076-9|web]].
  - 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 [[https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12410?casa_token=zXjhr13lURAAAAAA%3AU0nfTnrvO6QUugD6SR4O76SpwSeTLGI_UqjaoWgQk9yX_8CQJnTD9iwBhWQRmfA-hSR9wjOlTcnLXoM|web]].
  - 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 [[https://arxiv.org/pdf/2208.14721|web]].
  - 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 [[https://arxiv.org/pdf/2208.14721|web]].
  - S. Ouadah. Uniform-in-bandwidth nearest-neighbor density estimation. Statistics and Probability Letters, 83(8), 1835-1843, 2013 [[https://arxiv.org/pdf/2208.14721|web]].
  - S. Ouadah. Uniform-in-bandwidth kernel estimation for censored data. Journal of Statistical Planning and Inference, 143(8), 1273-1284, 2013 [[https://www.sciencedirect.com/science/article/abs/pii/S0167715213001351|web]].
  - P. Deheuvels, S. Ouadah. Uniform-in-bandwidth functional limit laws. Journal of Theoretical Probability, 26(3), 697-721, 2013 [[https://link.springer.com/article/10.1007/s10959-011-0376-1|web]].
  - P. Adamic, S. Ouadah. A kernel method for modelling interval censored competing risks. South African Statistical Journal, 43(1), 1-19, 2009 [[https://journals.co.za/doi/abs/10.10520/EJC119265|web]].  
 
**<color #00a2e8>R Packages</color>**
  - 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/.
  - 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.