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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Vydáno v:Research in the mathematical sciences Ročník 11; číslo 4
Hlavní autoři: Kou, Junke, Huang, Qinmei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2024
Témata:
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
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Shrnutí: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