Wavelet-based estimation of regression function for dependent biased data under a given random design

摘要

In this article, we consider the estimation of the regression function in a dependent biased model. It is assumed that the observations form a stationary α-mixing sequence. We introduce a new estimator based on a wavelet basis. We explore its asymptotic performances via the supremum norm error and the mean integrated squared error. Fast rates of convergence are established.