Publications

Submitted papers

• [S1] Modelling digit preference in ecological count data: A Bayesian approach. M. Authier, I. Albert, D. Ba, S. Donnet, M. Genu, E. Parent. Major revision at Journal of Agricultural, Biological, and Environmental Statistics

• [S2] Linking seed networks and crop diversity contributions to people: a case study in small-scale farming systems in Sahelian Senegal. Vanesse Labeyrie, Sophie Donnet; Rachel S. Friedman; Ndeye Fatou Faye; Océane Cobelli; Jacopo Baggio; Kailin Kroetz; María R. Felipe-Lucia; Jaime Ashander; Christine Raimond. Submitted to Agricultural Systems

• [S3] Learning common structures in a collection of networks. An application to food webs. S.-C. Chabert-Liddell, P. Barbillon, S. Donnet. Submitted. Available on Arxiv

Book chapters

• [C1] J. Aubert, P. Barbillon, S. Donnet, V.Miele. Chap Modèles à blocs latents pour la détection de structure dans les réseaux écologiques/Statistical Models for Hidden Variables in Ecology. Available @ ISTE.

Published or accepted articles

• [A23] Expert elicitation and hierarchical Bayesian approach for parametric survival models: An application to Ixodes ricinus ticks exposed to various temperature and relative humidity. P. Wongnak, S. Bord, S. Donnet, T. Hoch, F. Beugnet , K. Chalvet-Monfray. Ecological Modelling, Volume 464, February 2022

• [A22] Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling, S.-C. Chabert-Liddell, P. Barbillon, S. Donnet. Published in Environmetrics. DOI: 10.1002/env.2709

• [A21] Coupling ecological network analysis with high-throughput sequencing-based surveys: lessons from the Next-Generation Biomonitoring project, M. Dubart, P. Alonso, D. Barroso-Bergada, N. Becker, K. Bethune, D. A. Bohan, C. Boury, M. Cambon, E. Canard, E. Chancerel, J. Chiquet, P. David, N. de Manincor, S. Donnet, A. Duputié, B. Facon, E. Guichoux, T. Le Minh, S. Ortiz-Martínez, L. Piouceau, A. Sacco–Martret de Préville, M. Plantegenest, C. Poux, V. Ravigné, S. Robin, M. Trillat, C. Vacher, C. Vernière, François Massol. Accepted by Applied Ecology Resources

• [A20] Accelerating Bayesian estimation for network Poisson models using frequentist variational estimates. Sophie Donnet, Stéphane Robin. JRSSC, 16 April 2021. [Arxiv].

• [A19] A Stochastic Block Model for Multilevel Networks: Application to the Sociology of Organisations. Saint-Clair Chabert-Liddell, Pierre Barbillon, Sophie Donnet & Emmanuel Lazega Computational Statistics and Data Analysis, Volume 158, june 2021. [Arxiv].

• [A18] Block models for multipartite networks. Applications in ecology and ethnobiology. A. Bar-Hen, P. Barbillon & S. Donnet. Statistical Modelling, First Published December 18, 2020. [Arxiv]

• [A17] Nonparametric Bayesian estimation of multivariate Hawkes processes. S. Donnet, V. Rivoirard & J. Rousseau. Annals of Statistics, 2020, Vol. 48, No. 5, 2698-2727. [Arxiv]

• [A16] Effects of competition on collective learning in advice networks. E. Lazega, A. Bar-Hen, P. Barbillon, S. Donnet. Social Networks, Volume 47, October 2016, Pages 1–14 pdf

• [A15] Stochastic block models for multiplex networks: an application ot multilevel network of researchers. P. Barbillon, S. Donnet, E. Lazega, A. Bar-Hen. JRSSA. Volume 180, Issue1, January 2017, Pages 295-314 [Arxiv]

• [A14] * Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures.* S. Donnet, V. Rivoirard, J. Rousseau, C. Scricciolo. Bernoulli Journal 24(1): 231-256 (February 2018).

• [A13] S. Donnet, V. Rivoirard, J. Rousseau, C. Scricciolo. Posterior concentration rates for counting processes with Aalen multiplicative intensities. Bayesian Analysis 12(1): 53-87 (March 2017).

• [A12] M. Capistran, A. Christen, S. Donnet. Bayesian Analysis of ODE’s : solver optimal accuracy and Bayes factors. Journal of Uncertainty Quantification, 4, 829-849. pdf

• [A11] S. Donnet, J. Rousseau, J. Bayesian Inference for Partially Observed Branching Processes. Bayesian Analysis Volume 11, Number 1 (2016), 151-190

• [A10] S. Donnet, Bartolo, R., Fernandes J.M., Cunha J.P., Prado, L. and Merchant, H. Monkeys time their movement pauses and not their movement kinematics during a synchronization-continuation rhythmic task. Journal Of Neurophysiology, May 2014 ; 111(10), 2138 pdf

• [A9] S. Donnet, A. Samson. Using PMCMC in EM algorithm for stochastic mixed models : theoretical and practical issues. Journal de la Société Française de Statistique, 155, 49-72, 2014.

• [A8] S. Donnet, A. Samson. A review on estimation of stochastic differential equations for pharmacokinetic/ pharmacodynamic models. Advanced Drug Delivery Reviews pdf

• [A7] I. Albert, S. Donnet, C. Guihenneuc, S. Low-Choy, K. Mengersen, J. Rousseau, Combining expert opinions in prior elicitation (with discussion). Bayesian Analysis, 7(3), 503-546 (2012)

• [A6] S. Donnet, J.-M. Marin . An empirical Bayes procedure for the selection of Gaussian graphical models. Statistics and Computing, 22(5), 1113-1123 (2012) pdf

• [A5] S. Donnet , J-L Foulley, A. Samson. Bayesian analysis of growth curves using mixed models defined by stochastic differential equations . Biometrics, 66(3) :733-741, (2010) pdf

• [A4] P. Ciuciu, T. Vincent, L. Risser, S. Donnet. A joint detection-estimation framework for analysing within-subject fMRI data. Journal de la Société Française de Statistiques, Vol. 151, No 1 (2010)

• [A3] S. Donnet, A. Samson. Parametric inference for mixed models defined by stochastic differential equations. ESAIM P&S, 12 :196-218, (2008) pdf

• [A2] S. Donnet, A. Samson. Estimation of parameters in missing data models defined by differential equations. J. Statist. Plann. Inference 137 (2007), no. 9, 2815–2831 pdf

• [A1] S. Donnet, M. Lavielle, and J.-B. Poline. Are fMRI event related reponses constant across events ?. Neuroimage,Volume 31, Issue 3, 1 (July 2006), 1169 – 1176

Book reviews and discussions

• [B3] S. Donnet. Book review of “Stochastic Modelling for Systems Biology (second edition)” by Darren J. Wilkinson. CHANCE 25-4 (Décembre 2012)

• [B2] S. Donnet. Book review of “Statistical Thinking in Epidemiology” by Yu-Kang Tu and Mark S. Gilthorpe. CHANCE 25-4 (Décembre 2012)

• [B1] S. Donnet. Book review of “Monte Carlo Simulation for the Pharmaceutical Industry : Concepts, Algorithms, and Case Studies” by Mark Chang. International Statistical Review (Avril 2012)

• [D1] S. Donnet, A. Samson. Discussion on “Parameter estimation for differential equations : a generalized smoothing approaché” (by Ramsay JO, Hooker G, Campbell D and Cao J), Journal of the Royal Statistical Society : Series B, 69(5) :741-796, (2007)