Large and moderate deviations principles for kernel estimators of the multivariate regression
| dc.creator | Mokkadem, Abdelkader | |
| dc.creator | Pelletier, Mariane | |
| dc.creator | Thiam, Baba | |
| dc.date | 2007-03-12 | |
| dc.date.accessioned | 2026-07-07T08:08:51Z | |
| dc.date.available | 2026-07-07T08:08:51Z | |
| dc.description | In this paper, we prove large deviations principle for the Nadaraya-Watson estimator and for the semi-recursive kernel estimator of the regression in the multidimensional case. Under suitable conditions, we show that the rate function is a good rate function. We thus generalize the results already obtained in the unidimensional case for the Nadaraya-Watson estimator. Moreover, we give a moderate deviations principle for these two estimators. It turns out that the rate function obtained in the moderate deviations principle for the semi-recursive estimator is larger than the one obtained for the Nadaraya-Watson estimator. | |
| dc.description | 31 pages | |
| dc.identifier | https://arxiv.org/abs/math/0703341 | |
| dc.identifier | http://arxiv.org/abs/math/0703341 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/131403 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G08, 60F10 | |
| dc.title | Large and moderate deviations principles for kernel estimators of the multivariate regression | |
| dc.type | text |