Adaptive wavelet estimation of a function in an indirect regression model
نویسنده
چکیده
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 minimax approach under the mean integrated squared error over Besov balls, we prove that it attains a sharp rate of convergence.
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