Maxiset in sup-norm for kernel estimators
| dc.creator | Bertin, Karine | |
| dc.creator | Rivoirard, Vincent | |
| dc.date | 2007-01-16 | |
| dc.date.accessioned | 2026-07-07T08:08:37Z | |
| dc.date.available | 2026-07-07T08:08:37Z | |
| dc.description | In the Gaussian white noise model, we study the estimation of an unknown multidimensional function $f$ in the uniform norm by using kernel methods. The performances of procedures are measured by using the maxiset point of view: we determine the set of functions which are well estimated (at a prescribed rate) by each procedure. So, in this paper, we determine the maxisets associated to kernel estimators and to the Lepski procedure for the rate of convergence of the form $(\log n/n)^{\be/(2\be+d)}$. We characterize the maxisets in terms of Besov and Hölder spaces of regularity $β$. | |
| dc.description | 25 pages | |
| dc.identifier | https://arxiv.org/abs/math/0701446 | |
| dc.identifier | http://arxiv.org/abs/math/0701446 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/131325 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G07, 62G20 | |
| dc.title | Maxiset in sup-norm for kernel estimators | |
| dc.type | text |