The Performance of Cross-Validation and Maximum Likelihood Estimators of Spline Smoothing Parameters
通过大量模拟研究，比较了广义交叉验证、交叉验证和边际似然估计量在样条平滑中估计平滑参数的表现，发现5次样条的边际似然方法通常更优且计算更快。
Abstract An important aspect of nonparametric regression by spline smoothing is the estimation of the smoothing parameter. In this article we report on an extensive simulation study that investigates the finite-sample performance of generalized cross-validation, cross-validation, and marginal likelihood estimators of the smoothing parameter in splines of orders 2 and 3. The performance criterion for both the estimate of the function and its first derivative is measured by the square root of integrated squared error. Marginal likelihood using splines of degree 5 emerges as an attractive alternative to the other estimators in that it usually outperforms them and is also faster to compute.