Wavelet estimation of a regression model with mixed noises

Chesneau et al. ( Journal of Computational and Applied Mathematics , 2020) study nonparametric wavelet estimations over L 2 risk of a regression model with additive and multiplicative noises. This paper considers convergence rates over L p ( 1 ≤ p < + ∞ ) risk of linear wavelet estimator and nonl...

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Bibliographic Details
Published in:	Research in the mathematical sciences Vol. 11; no. 4
Main Authors:	Kou, Junke, Huang, Qinmei
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2024
Subjects:	Applications of Mathematics Computational Mathematics and Numerical Analysis Mathematics Mathematics and Statistics Wavelets 62G07 Nonparametric regression estimation 42C40 Mixed noises 62G20 risk
ISSN:	2522-0144, 2197-9847
Online Access:	Get full text
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Summary:	Chesneau et al. ( Journal of Computational and Applied Mathematics , 2020) study nonparametric wavelet estimations over L 2 risk of a regression model with additive and multiplicative noises. This paper considers convergence rates over L p ( 1 ≤ p < + ∞ ) risk of linear wavelet estimator and nonlinear wavelet estimator under some mild conditions. It turns out that our results reduce to the theorems of Chesneau et al., when p = 2 .
ISSN:	2522-0144 2197-9847
DOI:	10.1007/s40687-024-00481-8