AN EFFICIENT ALGORITHM FOR THE NONPARAMETRIC REGRESSION ESTIMATION

Most bandwidth selectors in nonparametric kernel regression estimation are based on minimization of some function of h, which is related to the resubstitution estimate of the prediction error. But this method consumes large amounts of computer time. This article concerns an efficient computational algorithm for this method when the kernel is symmetric and polynomial functions.