A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors
Methodology
2025-05-13 v1
Abstract
In this paper we propose a shrinkage wavelet-based method to estimate the signal in a nonparametric regression model with Autoregressive Fractionally Integrated Moving Average (ARFIMA) errors. Monte Carlo experiments indicate that the proposed method is better than the universal thresholding rule which is widely used in data analysis via wavelet regression models.
Cite
@article{arxiv.2505.06485,
title = {A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors},
author = {Alex Rodrigo dos S. Sousa and Mauricio Zevallos},
journal= {arXiv preprint arXiv:2505.06485},
year = {2025}
}