The Berry-Esseen bounds of wavelet estimator for regression model whose errors form a linear process with a ρ-mixing

In this paper, considering the nonparametric regression model Y-ni = g(t(i)) + epsilon(i) (1 <= i <= n), where epsilon(i) = Sigma(infinity)(j=-infinity) a(j)e(i-j) and e(i-j) are identically distributed and rho-mixing sequences. This paper obtains the Berry-Esseen bounds of the wavelet estimator of g(.), the rates of the normal approximation are shown as O(n(-1/6)) under certain conditions.