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On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise

  • Yuncai Yu , Xinsheng Liu EMAIL logo , Ling Liu and Weisi Liu
Published/Copyright: December 22, 2019
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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.

  1. Communicated by Gejza Wimmer

Acknowledgement

This work is supported by National Natural Science Foundation of China [No. 61374183, 51535005], and the Postgraduate Research & Practice Innovation Program of Jiangsu Province [No. KYCX19_0149].

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Received: 2018-09-27
Accepted: 2019-03-19
Published Online: 2019-12-22
Published in Print: 2019-12-18

© 2019 Mathematical Institute Slovak Academy of Sciences

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