Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators
Statistics Theory
2016-08-16 v1 Statistics Theory
Abstract
We study the performances of an adaptive procedure based on a convex combination, with data-driven weights, of term-by-term thresholded wavelet estimators. For the bounded regression model, with random uniform design, and the nonparametric density model, we show that the resulting estimator is optimal in the minimax sense over all Besov balls under the risk, without any logarithm factor.
Cite
@article{arxiv.math/0612546,
title = {Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators},
author = {Christophe Chesneau and Guillaume Lecué},
journal= {arXiv preprint arXiv:math/0612546},
year = {2016}
}