On the convergence rate in the central limit theorem for linearly extended negative quadrant dependent random variables and its applications

In this paper, we establish the convergence rate in central limit theorem (CLT) for linearly extended negative quadrant dependent (LENQD) random variables (rv’s). Under some weak conditions, the rate of normal approximation is shown as $O(n^{-1\mathrm{/}9})$. As an application, the convergence rate in CLT of the wavelet estimator for the nonparametric regression model with LENQD errors is presented as $O(n^{-1\mathrm{/}9})$. The performance of the main results is illustrated through a simulation study based on a real dataset.