Robustness of one-sided cross-validation to autocorrelation

12Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.

Abstract

The effects of moderate levels of serial correlation on one-sided and ordinary cross-validation in the context of local linear and kernel smoothing is investigated. It is shown both theoretically and by simulation that one-sided cross-validation is much less adversely affected by correlation than is ordinary cross-validation. The former method is a reliable means of window width selection in the presence of moderate levels of serial correlation, while the latter is not. It is also shown that ordinary cross-validation is less robust to correlation when applied to Gasser-Müller kernel estimators than to local linear ones. © 2003 Elsevier Inc. All rights reserved.

Cite

CITATION STYLE

APA

Hart, J. D., & Lee, C. L. (2005). Robustness of one-sided cross-validation to autocorrelation. Journal of Multivariate Analysis, 92(1), 77–96. https://doi.org/10.1016/j.jmva.2003.08.005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free