One-sided cross-validation Academic Article uri icon

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. 1998 Taylor & Francis Group, LLC.

published proceedings

  • JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

author list (cited authors)

  • Hart, J. D., & Yi, S.

citation count

  • 50

complete list of authors

  • Hart, JD||Yi, S

publication date

  • June 1998