Exact oracle inequality for a sharp adaptive kernel density estimator
| dc.creator | Dalelane, Clementine | |
| dc.date | 2005-04-19 | |
| dc.date.accessioned | 2026-07-07T08:06:48Z | |
| dc.date.available | 2026-07-07T08:06:48Z | |
| dc.description | In one-dimensional density estimation on i.i.d. observations we suggest an adaptive cross-validation technique for the selection of a kernel estimator. This estimator is both asymptotic MISE-efficient with respect to the monotone oracle, and sharp minimax-adaptive over the whole scale of Sobolev spaces with smoothness index greater than 1/2. The proof of the central concentration inequality avoids "chaining" and relies on an additive decomposition of the empirical processes involved. | |
| dc.identifier | https://arxiv.org/abs/math/0504382 | |
| dc.identifier | http://arxiv.org/abs/math/0504382 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/130734 | |
| dc.subject | Statistics Theory | |
| dc.subject | MSC: 62G07, 62G20 | |
| dc.title | Exact oracle inequality for a sharp adaptive kernel density estimator | |
| dc.type | text |