Curriculum vitae

Rivoirard Vincent

Professeur des universités
CEREMADE

rivoirardping@ceremade.dauphinepong.fr
Tel : 01 44 05 44 00
Bureau : B 636
Site web personnel

Biographie

Vincent Rivoirard est Professeur d'Université à l'Université Paris Dauphine depuis 2010 après avoir été Maître de Conférences à l'Université Paris Sud Orsay entre 2003 et 2010. Il a réalisé une thèse de statistique sous la direction de Dominique Picard, soutenue en 2002. Ses intérêts de recherche portent sur la statistique non-paramétrique et en grandes dimensions pour l'estimation bayésienne et fréquentiste. Il s'intéresse aux applications statistiques en neurosciences, en génétique et en biologie. Il est directeur du Ceremade depuis le 1er novembre 2016.

Biographie complète disponible sur : https://www.ceremade.dauphine.fr/~rivoirar/

Dernières publications

Articles

Browning R., Sulem D., Mengersen K., Rivoirard V., Rousseau J. (2021), Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19, PLoS ONE, vol. 16, n°4

Donnet S., Rivoirard V., Rousseau J. (2020), Nonparametric Bayesian estimation for multivariate Hawkes processes, Annals of Statistics, vol. 48, n°5, p. 2698-2727

Hunt X., Reynaud-Bouret P., Rivoirard V., Sansonnet L., Willett R. (2019), A Data-Dependent Weighted LASSO Under Poisson Noise, IEEE Transactions on Information Theory, vol. 65, n°3, p. 1589-1613

Lambert R., Tuleau-Malot C., Bessaih T., Rivoirard V., Bouret Y., Leresche N., Reynaud-Bouret P. (2018), Reconstructing the functional connectivity of multiple spike trains using Hawkes models, Journal of Neuroscience Methods, vol. 297, n°1 March 2018, p. 9-21

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2018), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures, Bernoulli, vol. 24, n°1, p. 231-256

Chichignoud M., Hoang V., Pham Ngoc T., Rivoirard V. (2017), Adaptive wavelet multivariate regression with errors in variables, Electronic Journal of Statistics, vol. 11, n°1, p. 682-724

Lacour C., Massart P., Rivoirard V. (2017), Estimator selection: a new method with applications to kernel density estimation, Sankhya, vol. 79, n°2, p. 298-335

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2017), Posterior concentration rates for counting processes with Aalen multiplicative intensities, Bayesian Analysis, vol. 12, n°1, p. 53-87

Bertin K., Lacour C., Rivoirard V. (2016), Adaptive pointwise estimation of conditional density function, Annales Henri Poincaré, vol. 52, n°2, p. 939-980

Ivanoff S., Picard F., Rivoirard V. (2016), Adaptive Lasso and group-Lasso for functional Poisson regression, Journal of Machine Learning Research, vol. 17, p. 1-46

Hansen N., Reynaud-Bouret P., Rivoirard V. (2015), Lasso and probabilistic inequalities for multivariate point processes, Bernoulli, vol. 21, n°1, p. 83-143

Arribas-Gil A., Bertin K., Rivoirard V., Meza C. (2014), LASSO-type estimators for Semiparametric Nonlinear Mixed-Effects Models Estimation, Statistics and Computing, vol. 24, n°3, p. 443-460

Grammont F., Tuleau-Malot C., Rivoirard V., Reynaud-Bouret P. (2014), Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis, The Journal of Mathematical Neuroscience, vol. 4, n°1

Pham Ngoc T., Rivoirard V. (2013), The dictionary approach for spherical deconvolution, Journal of Multivariate Analysis, vol. 115, p. 138-156

Rivoirard V., Rousseau J. (2012), Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, n°2, p. 311-334

Rivoirard V., Reynaud-Bouret P., Hoffmann M., Doumic Jauffret M. (2012), Nonparametric estimation of the division rate of a size-structured population, SIAM Journal on Numerical Analysis, vol. 50, n°2, p. 925-950

Rousseau J., Rivoirard V. (2012), Bernstein–von Mises theorem for linear functionals of the density, Annals of Statistics, vol. 40, n°3, p. 1489-1523

Reynaud-Bouret P., Rivoirard V., Tuleau-Malot C. (2011), Adaptive density estimation: A curse of support?, Journal of Statistical Planning and Inference, vol. 141, n°1, p. 115-139

Bertin K., Le Pennec E., Rivoirard V. (2011), Adaptive Dantzig density estimation, Annales Henri Poincaré, vol. 47, n°1, p. 43-74

Autin F., Le Pennec E., Loubes J., Rivoirard V. (2010), Maxisets for Model Selection, Constructive Approximation, vol. 31, n°2, p. 195-229

Reynaud-Bouret P., Rivoirard V. (2010), Near optimal thresholding estimation of a Poisson intensity on the real line, Electronic Journal of Statistics, vol. 4, p. 172-238

Bertin K., Rivoirard V. (2009), Maxiset in sup-norm for kernel estimators, Test, vol. 18, n°3, p. 475-496

Loubes J-M., Rivoirard V. (2009), Review of rates of convergence and regularity conditions for inverse problems, International Journal of Tomography & Statistics, vol. 11, n°S09

Ouvrages

Stoltz G., Rivoirard V. (2009), Statistique en action, Paris: Vuibert, 320 p.

Communications avec actes

Reynaud-Bouret P., Rivoirard V., Tuleau-Malot C. (2013), Inference of functional connectivity in Neurosciences via Hawkes processes, in , Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, Austin, IEEE - Institute of Electrical and Electronics Engineers

Communications sans actes

Donnet S., Rousseau J., Rivoirard V., Scricciolo C. (2014), On Convergence Rates of Empirical Bayes Procedures, SIS 2014, Cagliari, Italie

Donnet S., Rousseau J., Rivoirard V. (2014), Non parametric Bayesian estimation for Hawkes processes, International Society for Bayesian Analysis World Meeting, ISBA 2014, Cancun, Mexique

Malot C., Reynaud-Bouret P., Rivoirard V., Grammont F. (2013), Tests d'adéquation pour les processus de Poisson et les processus de Hawkes, 45ème Journées de Statistique, Toulouse, France

Prépublications / Cahiers de recherche

Belhakem M., Picard F., Rivoirard V., Roche A. (2021), Minimax estimation of Functional Principal Components from noisy discretized functional data, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 35 p.

BONNET A., Lacour C., Picard F., Rivoirard V. (2020), Uniform Deconvolution for Poisson Point Processes, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 32 p.

Varet S., Lacour C., Massart P., Rivoirard V. (2019), Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 50 p.

Hoang V., Pham Ngoc T., Rivoirard V., Tran V. (2017), Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 29 p.

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2014), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures. Supplementary material, Paris, Université Paris-Dauphine, 4 p.

Rapports

Nguyen M-L., Lacour C., Rivoirard V. (2019), Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 45 p.

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