Adaptive wavelet estimation of a function in an indirect regression model
Résumé
We consider a nonparametric regression model where m noiseperturbed functions f1, . . . , fm are randomly observed. For a fixed ν ∈ {1, . . . ,m}, we want to estimate fν from the observations. To reach this goal, we develop an adaptive wavelet estimator based on a hard thresholding rule. Adopting the mean integrated squared error over Besov balls, we prove that it attains a sharp rate of convergence. Simulation results are reported to support our theoretical findings.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |