V. Rivoirard

发表

BERNSTEIN-VON MISES THEOREM FOR LINEAR FUNCTIONALS OF THE DENSITY

J. Rousseau, V. Rivoirard, 2009, 0908.4167.

Adaptive Dantzig density estimation

E. L. Pennec, Karine Bertin, V. Rivoirard, 2009, 0905.0884.

Near optimal thresholding estimation of a Poisson intensity on the real line

P. Reynaud-Bouret, V. Rivoirard, 2008, 0810.5204.

Adaptive density estimation: a curse of support? ✩

P. Reynaud-Bouret, V. Rivoirard, Christine Tuleau-Malot, 2009, 0907.1794.

Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation

V. Tran, V. Rivoirard, V. Hoang, 2017, Scandinavian Journal of Statistics.

Posterior concentration rates for infinite dimensional exponential families

Judith Rousseau, Vincent Rivoirard, J. Rousseau, 2012 .

Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures

Judith Rousseau, Catia Scricciolo, Vincent Rivoirard, 2014, 1406.4406.

Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation

P. Massart, C. Lacour, S. Varet, 2019, ESAIM: Probability and Statistics.

Adaptive estimation of conditional density function

C. Lacour, Karine Bertin, V. Rivoirard, 2013 .

Adaptive pointwise estimation of conditional density function

Vincent Rivoirard, Claire Lacour, Karine Bertin, 2013, 1312.7402.

Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation

C. Lacour, V. Rivoirard, Minh Nguyen, 2019, J. Mach. Learn. Res..

Posterior Concentration Rates for Counting Processes with Aalen Multiplicative Intensities

J. Rousseau, S. Donnet, Catia Scricciolo, 2014, 1407.6033.

Review of rates of convergence and regularity conditions for inverse problems

Jean-Michel Loubes, V. Rivoirard, 2009 .

1406 . 4406 v 1 Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures SOPHIE

J. Rousseau, Catia Scricciolo, V. Rivoirard, 2016 .

Thresholding procedure with priors based on Pareto distributions

V. Rivoirard, 2004 .

BAYESIAN MODELLING OF SPARSE SEQUENCES AND MAXISETS FOR BAYES RULES

V. Rivoirard, 2003 .

On Convergence Rates of Empirical Bayes Procedures

Judith Rousseau, Catia Scricciolo, Vincent Rivoirard, 2014 .

Calibration of thresholding rules for Poisson intensity estimation

P. Reynaud-Bouret, V. Rivoirard, 2009, 0904.1148.

Maxiset comparisons of procedures, application to choosing priors in a Bayesian nonparametric setting

D. Picard, F. Autin, V. Rivoirard, 2004 .

Large variance Gaussian priors in Bayesian nonparametric estimation: a maxiset approach

D. Picard, F. Autin, V. Rivoirard, 2006 .

Uniform Deconvolution for Poisson Point Processes

C. Lacour, V. Rivoirard, F. Picard, 2020, 2010.04557.

Maxiset in sup-norm for kernel estimators

Karine Bertin, V. Rivoirard, K. Bertin, 2007, math/0701446.

Lasso and probabilistic inequalities for multivariate point processes

P. Reynaud-Bouret, V. Rivoirard, N. Hansen, 2012, 1208.0570.

Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation

V. Tran, V. Rivoirard, T. Ngoc, 2021 .

Adaptive thresholding estimation of a Poisson intensity with infinite support

P. Reynaud-Bouret, V. Rivoirard, 2008, 0801.3157.

Adaptive wavelet multivariate regression with errors in variables

M. Chichignoud, V. Rivoirard, T. Ngoc, 2016, 1601.02762.

Scalable and adaptive variational Bayes methods for Hawkes processes

J. Rousseau, V. Rivoirard, Deborah Sulem, 2022, 2212.00293.

Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19

Kerrie Mengersen, R Browning, D Sulem, 2020, PloS one.

A Data-Dependent Weighted LASSO Under Poisson Noise

Rebecca Willett, Vincent Rivoirard, Laure Sansonnet, 2015, IEEE Transactions on Information Theory.

Genomic transcription regulatory element location analysis via poisson weighted lasso

Xin Jiang, Rebecca Willett, Vincent Rivoirard, 2016, 2016 IEEE Statistical Signal Processing Workshop (SSP).

Adaptive Lasso and group-Lasso for functional Poisson regression

Franck Picard, Vincent Rivoirard, Stéphane Ivanoff, 2014, J. Mach. Learn. Res..

LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation

Ana Arribas-Gil, Vincent Rivoirard, Cristian Meza, 2012, Stat. Comput..

Nonlinear estimation over weak Besov spaces and minimax Bayes method

Vincent Rivoirard, V. Rivoirard, 2006 .

Adaptive Supremum Norm Posterior Contraction : Spike-and-Slab Priors and Anisotropic Besov Spaces

J. Rousseau, V. Rivoirard, W. Yoo, 2017 .

Estimator Selection: a New Method with Applications to Kernel Density Estimation

Pascal Massart, CEREMADE, Claire Lacour, 2016, Sankhya A.

Maxisets for Model Selection

Jean-Michel Loubes, E. L. Pennec, F. Autin, 2008, 0802.4192.

MAXISETS FOR MODEL SELECTION 3 select an estimate among the set of the estimates

Jean-Michel Loubes, E. L. Pennec, V. Rivoirard, 2008 .

Nonparametric Estimation of the Division Rate of a Size-Structured Population

Marie Doumic, M. Hoffmann, Patricia Reynaud-Bouret, 2011, SIAM J. Numer. Anal..

Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis

Franck Grammont, Patricia Reynaud-Bouret, Vincent Rivoirard, 2014, The Journal of Mathematical Neuroscience.

Inference of functional connectivity in Neurosciences via Hawkes processes

Vincent Rivoirard, Christine Tuleau-Malot, Patricia Reynaud-Bouret, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

Minimax estimation of Functional Principal Components from noisy discretized functional data

Franck Picard, Vincent Rivoirard, Angelina Roche, 2021, 2110.12739.

Statistical deconvolution of the free Fokker-Planck equation at fixed time

V. Tran, M. Maida, V. Rivoirard, 2020, Bernoulli.

Spike trains as (in)homogeneous Poisson processes or Hawkes processes: non-parametric adaptive estimation and goodness-of-fit tests

Franck Grammont, Vincent Rivoirard, Christine Tuleau-Malot, 2013 .

Reconstructing the functional connectivity of multiple spike trains using Hawkes models

Patricia Reynaud-Bouret, Vincent Rivoirard, Christine Tuleau-Malot, 2017, Journal of Neuroscience Methods.

Eve, Adam and the preferential attachment tree

V. Rivoirard, N. Curien, E. Lasalle, 2023, Probability Theory and Related Fields.

Nonparametric Bayesian estimation for multivariate Hawkes processes

Judith Rousseau, Vincent Rivoirard, Sophie Donnet, 2018, The Annals of Statistics.

Scalable and adaptive variational Bayes methods for Hawkes processes

J. Rousseau, V. Rivoirard, Déborah Sulem, 2022, 2212.00293.

The dictionary approach for spherical deconvolution

Vincent Rivoirard, Thanh Mai Pham Ngoc, V. Rivoirard, 2013, J. Multivar. Anal..

Modeling Spatial Genomic Interactions with the Hawkes model

Franck Picard, A Bonnet, V Rivoirard, 2017, bioRxiv.

Adaptive warped kernel estimation for nonparametric regression with circular responses

V. Rivoirard, T. Nguyen, T. Ngoc, 2022, Electronic Journal of Statistics.