Common Structure of Techniques for Choosing Smoothing Parameters in Regression Problems
研究了线性岭回归和非参数回归中两种常见的平滑参数选择方法：最小风险准则和基于残差统计量的拟合优度度量，发现第二种方法的某个流行版本会过度平滑，并提出了替代方案。
SUMMARY Two general methods have been used for choosing the degree of smoothing in both linear ridge-regression and nonparametric regression. One is based on a criterion of minimum risk and the other is based on a measure of fit to the data as summarised by a statistic based on the residuals. The comparative effects of these two approaches are investigated and one popular version of the second method is shown to oversmooth quite drastically. The work also generates alternative suggestions for data-based smoothing prescriptions, and elucidates the heuristic argument of Wahba (1983).