A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors
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 widely used wavelet shrinkage methods, and is the best in situations where the noise has very long memory.
Authors
- Mauricio Zevallos (ORCID: https://orcid.org/0000-0001-9911-7249)
- Alex Rodrigo dos S. Sousa
Institutions
- Universidade Estadual de Campinas (UNICAMP) (BR)
Publication Details
- Journal
- Communications in Statistics - Simulation and Computation
- Published
- 2026-10-29
- DOI
- https://doi.org/10.1080/03610918.2026.2753584
- Primary Topic
- Statistical Methods and Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Conselho Nacional de Desenvolvimento Científico e Tecnológico