2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Minimax estimation of Functional Principal Components from noisy discretized functional data: the case of smooth processes
Nassim Bourarach, Franck Picard, Vincent Rivoirard +1
We study the minimax estimation of covariance eigenfunctions and eigenvalues in functional principal component analysis when trajectories are observed at common grid points…
Non-asymptotic two-sample kernel testing with the spectrally truncated normalized MMD
Perrine Lacroix, Bertrand Michel, Franck Picard +1
Kernel methods provide a flexible and powerful framework for nonparametric statistical testing by embedding probability distributions into a reproducing kernel Hilbert space (RKHS)…
Concentration inequalities and cut-off phenomena for penalized model selection within a basic Rademacher framework
Pascal Massart, Vincent Rivoirard
This article exists first and foremost to contribute to a tribute to Patrick Cattiaux. One of the two authors has known Patrick Cattiaux for a very long time, and owes him a great…
PCA for Point Processes
Franck Picard, Vincent Rivoirard, Angelina Roche +1
We introduce a novel statistical framework for the analysis of replicated point processes that allows for the study of point pattern variability at a population level. By treating…
Adaptive Dantzig density estimation
Karine Bertin, Erwan Le Pennec, Vincent Rivoirard
This paper deals with the problem of density estimation. We aim at building an estimate of an unknown density as a linear combination of functions of a dictionary. Inspired by Cand…