One-Sided Cross-Validation
提出一种新的非参数回归平滑参数选择方法——单侧交叉验证，兼具交叉验证的客观性和插件法的统计性质，并通过模拟和理论证明其有效性。
Abstract A new method of selecting the smoothing parameters of nonparametric regression estimators is introduced. The method, termed one-sided cross-validation (OSCV), has the objectivity of cross-validation and statistical properties comparable to those of a plug-in rule. The new method may be viewed as an application of the prequential model selection method of Dawid. As such, our results identify a situation in which the prequential method is a more efficient model selector than cross-validation. An example, simulations, and theoretical results demonstrate the utility of OSCV when used with local linear and kernel estimators.