One-Sided Cross-Validation
One-Sided Cross-Validation
复制标题
单方面交叉验证
DOI：
10.1080/01621459.1998.10473715
复制
发表时间：
1998
影响因子：
3.7
通讯作者：
Seongbaek Yi
中科院分区：
文献类型：
--
作者：
J. Hart;Seongbaek Yi
摘要介绍了非参数回归估计中光滑参数选取的一种新方法。该方法称为单侧交叉验证（OSCV），具有交叉验证的客观性和与插件规则相当的统计特性。该方法可以看作是Dawid序贯模型选择方法的一个应用。因此，我们的研究结果确定了一种情况，其中的先决条件的方法是一个更有效的模型选择器比交叉验证。一个例子，模拟和理论结果表明，OSCV的效用时，与当地的线性和核估计。
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.