Nonparametric regression estimation for random fields in a fixed-design
| dc.creator | Machkouri, Mohamed El | |
| dc.date | 2005-02-04 | |
| dc.date.accessioned | 2026-07-07T08:06:41Z | |
| dc.date.available | 2026-07-07T08:06:41Z | |
| dc.description | We investigate the nonparametric estimation for regression in a fixed-design setting when the errors are given by a field of dependent random variables. Sufficient conditions for kernel estimators to converge uniformly are obtained. These estimators can attain the optimal rates of uniform convergence and the results apply to a large class of random fields which contains martingale-difference random fields and mixing random fields. | |
| dc.description | Accepté pour publication dans la revue "Statistical Inference for Stochastic Processes" | |
| dc.identifier | https://arxiv.org/abs/math/0502091 | |
| dc.identifier | http://arxiv.org/abs/math/0502091 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/130694 | |
| dc.subject | Statistics Theory | |
| dc.subject | Probability | |
| dc.subject | 60G60; 62G08 | |
| dc.title | Nonparametric regression estimation for random fields in a fixed-design | |
| dc.type | text |