Robustness of one-sided cross-validation to autocorrelation Academic Article uri icon


  • 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-Mller kernel estimators than to local linear ones. 2003 Elsevier Inc. All rights reserved.

published proceedings


author list (cited authors)

  • Hart, J. D., & Lee, C. L.

citation count

  • 17

complete list of authors

  • Hart, JD||Lee, CL

publication date

  • January 2005