Research themes:
- Nonparametric estimation
- Inverse problems
- Frequentist and Bayesian approaches
- High-dimensional statistics
- Wavelets and approximation theory
- Multivariate point processes
- Statistics for biology, genetics and neuroscience
SUBMITTED ARTICLES:
PUBLICATIONS:
- Sulem D., Rivoirard V. and Rousseau J. Scalable and adaptive variational Bayes methods for Hawkes processes (80 pages) To appear in Journal of Machine Learning Research
- Belhakem R., Picard F., Rivoirard V. and Roche A. (2025) Minimax estimation of Functional Principal Components from noisy discretized functional data. Published in Scandinavian Journal of Statistics, 52, no 1, 38-80
- Sulem D., Rivoirard V. and Rousseau J. (2024) Bayesian estimation of nonlinear Hawkes processes. Published in Bernoulli, 30, no 2, 1257-1286. Supplementary file (49 pages)
- Nguyen T.D., Pham Ngoc T.M. and Rivoirard V. (2023) Adaptive warped kernel estimation for nonparametric regression with circular responses. Published in Electronic Journal of Statistics, 17, no 2, 4011-4048
- Varet S, Lacour C., Massart P. and Rivoirard V. (2023) Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation. Published in ESAIM PS, 27, 621-667
- Nguyen M.L.J., Lacour C. and Rivoirard V (2022) Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation. Published in Journal of Machine Learning Research, 23, no 254, 1-74
- Hoang V.H., Pham Ngoc T.M., Rivoirard V. and Tran V.C. (2022) Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation. Published in Scandinavian Journal of Statistics, 49, no 1, 4-43
- Maïda M., Nguyen T.D., Pham Ngoc T.M., Rivoirard V. and Tran V.C. (2022) Statistical deconvolution of the free Fokker-Planck equation at fixed time. Published in Bernoulli, 28, no 2, 771-802. Supplementary file (13 pages)
- Bonnet A., Lacour C., Picard F. and Rivoirard V. (2022) Uniform Deconvolution for Poisson Point Processes. Published in Journal of Machine Learning Research, 23, no 194, 1-36
- Browning R., Sulem D., Mengersen K, Rivoirard V. and Rousseau J. (2021) Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. Published in PLoS ONE, 16, no 4, 1-28
- Donnet S., Rivoirard V. and Rousseau J. (2020) Nonparametric Bayesian estimation of multivariate Hawkes processes Published in Annals of Statistics, 48, no 5, 2698-2727. Supplementary file (29 pages)
- Hunt X. J., Reynaud-Bouret P., Rivoirard V., Sansonnet L. and Willet R. (2019) A data-dependent weighted LASSO under Poisson noise Published in IEEE Transactions on Information Theory, 65, no 3, 1589-1613
- Lambert R., Tuleau-Malot C., Bessaih T., Rivoirard V., Bouret Y., Leresche N. and Reynaud-Bouret P. (2018) Reconstructing the functional connectivity of multiple spike trains using Hawkes models Published in the Journal of Neuroscience Methods, 297, 9-21
- Donnet S., Rivoirard V., Rousseau J. and Scricciolo C. (2018) Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures Published in Bernoulli, 24, no 1, 231-256. Supplementary file (13 pages)
- Lacour C., Massart P. and Rivoirard V. (2017) Estimator selection: a new method with applications to kernel density estimation Published in Sankhya A (special issue on Application of concentration inequalities and empirical processes to modern statistics), 79, no 2, 298-335
- Chichignoud M., Hoang V.H., Pham Ngoc T.M. and Rivoirard V. (2017) Adaptive wavelet multivariate regression with errors in variables Published in Electronic Journal of Statistics, 11, no 1, 682-724
- Donnet S., Rivoirard V., Rousseau J. and Scricciolo C. (2017) Posterior concentration rates for counting processes with Aalen multiplicative intensities Published in Bayesian Analysis, 12, no 1, 53-87. Supplementary file (8 pages)
- Ivanoff S., Picard F. and Rivoirard V. (2016) Adaptive Lasso and group-Lasso for functional Poisson regression Published in Journal of Machine Learning Research, 17, no 55, 1-46
- Bertin K., Lacour C. and Rivoirard V. (2016) Adaptive pointwise estimation of conditional density function Published in Les Annales de l'IHP, 52, no 2, 939-980
- Hansen N.R., Reynaud-Bouret P. and Rivoirard V. (2015) Lasso and probabilistic inequalities for multivariate point processes Published in Bernoulli, 21, no 1, 83-143
- Reynaud-Bouret P., Rivoirard V., Grammont F. and Tuleau-Malot C. (2014) Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis Published in Journal of Mathematical Neuroscience, 4:3, 1-41. Supplementary file 1 (9 pages) Supplementary file 2 (5 pages) Supplementary file 3 (3 pages)
- Arribas-Gil A., Bertin K., Meza C. and Rivoirard V. (2014) Lasso-type estimators for semiparametric nonlinear mixed-effects models estimation Published in Statistics and Computing, 24, no 3, 443-460. Supplementary file (12 pages)
- Pham Ngoc T.M. and Rivoirard V. (2013) The dictionary approach for spherical deconvolution Published in Journal of Multivariate Analysis, 115, 138-156
- Rivoirard V. and Rousseau J. (2012) Bernstein - von Mises theorem for linear functionals of the density Published in Annals of Statistics, 40, no 3, 1489-1523
- Rivoirard V. and Rousseau J. (2012) Posterior concentration rates for infinite dimensional exponential families Published in Bayesian Analysis, 7, no 2, 311-334
- Doumic M., Hoffmann M., Reynaud-Bouret P. and Rivoirard V. (2012) Nonparametric estimation of the division rate of a size-structured population Published in SIAM Journal on Numerical Analysis, 50, no. 2, 925-950
- Bertin K., Le Pennec E. and Rivoirard V. (2011) Adaptive Dantzig density estimation Published in Les Annales de l'IHP, 47, no. 1, 43-74
- Reynaud-Bouret P., Rivoirard V. and Tuleau-Malot C. (2011) Adaptive density estimation: a curse of support? Published in Journal of Statistical Planning and Inference, 141, 115-139
- Reynaud-Bouret P. and Rivoirard V. (2010) Near optimal thresholding estimation of a Poisson intensity on the real line Published in Electronic Journal of Statistics, 4, 172-238
- Autin F., Le Pennec E., Loubes J.M. and Rivoirard V. (2010) Maxisets for model selection Published in Constructive approximation, 31, no. 2, 195-229
- Bertin K. and Rivoirard V. (2009) Maxiset in sup-norm for kernel estimators Published in Test, 18, no. 3, 475-496
- Loubes J.M. and Rivoirard V. (2009) Review of rates of convergence and regularity conditions for inverse problems Published in International Journal of Tomography & Statistics, 11, no. S09, 61-82
- Rivoirard V. and Tribouley K. (2008) The maxiset point of view for estimating integrated quadratic functionals Published in Statistica Sinica, 18, no. 1, 255-279
- Autin F., Picard D. and Rivoirard V. (2006) Large variance Gaussian priors in Bayesian nonparametric estimation: a maxiset approach Published in Mathematical Methods of Statistics, 15, no. 4, 349-373
- Rivoirard V. (2006) Non linear estimation over weak Besov spaces and minimax Bayes method Published in Bernoulli, 12, no. 4, 609-632
- Rivoirard V. (2005) Bayesian modelling of sparse sequences and maxisets for Bayes rules Published in Mathematical Methods of Statistics, 14, no. 3, 346-376.
- Rivoirard V. (2004) Thresholding procedure with priors based on Pareto distributions Published in Test, 13, no. 1, 213-246
- Rivoirard V. (2004) Maxisets for linear procedures Published in Statistics and Probability Letters, 67, no. 3, 267-275
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