| Clustering multivariate functional data using unsupervised binary trees S Golovkine, N Klutchnikoff, V Patilea Computational Statistics & Data Analysis 168, 107376, 2022 | 14 | 2022 |
| Learning the smoothness of noisy curves with application to online curve estimation S Golovkine, N Klutchnikoff, V Patilea Electronic Journal of Statistics 16 (1), 1485-1560, 2022 | 9 | 2022 |
| FDApy: a Python package for functional data S Golovkine arXiv preprint arXiv:2101.11003, 2021 | 4 | 2021 |
| Adaptive optimal estimation of irregular mean and covariance functions S Golovkine, N Klutchnikoff, V Patilea | 2 | 2021 |
| Statistical methods for multivariate functional Data S Golovkine Rennes, École Nationale de Statistique et Analyse de l'Information, 2021 | 1* | 2021 |
| On the estimation of the number of components in multivariate functional principal component analysis S Golovkine, E Gunning, AJ Simpkin, N Bargary arXiv preprint arXiv:2311.04540, 2023 | | 2023 |
| On the use of the Gram matrix for multivariate functional principal components analysis S Golovkine, E Gunning, AJ Simpkin, N Bargary arXiv preprint arXiv:2306.12949, 2023 | | 2023 |
| Adaptive estimation of irregular mean and covariance functions S Golovkine, N Klutchnikoff, V Patilea arXiv preprint arXiv:2108.06507, 2021 | | 2021 |
Valentin PatileaProfesseur of Statistics, CREST& EnsaiVerified email at ensai.fr
Valentin PatileaProfesseur of Statistics, CREST& EnsaiVerified email at ensai.fr