摘要：We consider the regression model with (known) random design. We investigate the minimax performances of an adaptive wavelet block thresholding estimator under the Lp risk with p >2 over Besov balls. We prove that it is near optimal and that it achieves better rates of convergence than the conventional term-by-term estimators (hard, soft,...).
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