Wavelet estimation of a regression model with mixed noises

Chesneau et al. (Journal of Computational and Applied Mathematics, 2020) study nonparametric wavelet estimations over \(L^{2}\) $L^2$ risk of a regression model with additive and multiplicative noises. This paper considers convergence rates over \(L^{p}(1\le p<+\infty )\) $L^p(1\le p<+\infty )$ 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 \(p=2\) $p=2$ .