Abstract
The smoothing method known as supersmoother does not always give sufficient results when the number of data is large or the local behavior of data changes dramatically. This paper, therefore, suggests that the prediction errors given by local cross-validation are weighted-averaged with larger weights in the neighborhood of estimation points, and the resultant values are minimized to optimize the values of the local smoothing parameter. For this purpose, the width of the area of neighborhood is determined by minimizing the prediction error in the entire area; this prediction error is calculated by the use of the hat matrix corresponding to the smoothing. Some results using simulation data shows that this new smoothing method performs better than conventional ones.
