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One-sided cross-validation
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AI
DOI：10.2307/2670113.png)
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Abstract
En 中文
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.
Keywords:
average squared error
data-driven smoothing
mean average squared error
optimal bandwidths
prediction
Journal
J
IF:
3
Papers:
5.2K
Citations:
4.8W
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