On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise
Statistics Theory
2019-10-10 v1 Statistics Theory
Abstract
This paper considers the nonparametric regression model with negatively super-additive dependent (NSD) noise and investigates the convergence rates of thresholding estimators. It is shown that the term-by-term thresholding estimator achieves nearly optimal and the block thresholding estimator attains optimal (or nearly optimal) convergence rates over Besov spaces. Additionally, some numerical simulations are implemented to substantiate the validity and adaptivity of the thresholding estimators with the presence of NSD noise.
Keywords
Cite
@article{arxiv.1910.03911,
title = {On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise},
author = {Yuncai Yu and Xinsheng Liu and Ling Liu and Weisi Liu},
journal= {arXiv preprint arXiv:1910.03911},
year = {2019}
}
Comments
14 pages;2figures