Asymptotic distribution of the wavelet-based estimators of multivariate regression functions under weak dependence
Résumé
This paper investigates the nonparametric linear wavelet-based estimators of multi- variate regression functions. Under mild conditions, we establish the asymptotic normality under the weak dependence, which incorporates mixing and association concepts. This framework ap- plies to numerous classes of intriguing statistical processes, primarily Gaussian sequences and, more generally, Bernoulli shifts. We give an application for the confidence interval.