Robustness of one-sided cross-validation to autocorrelation

Jeffrey D. Hart and Cherng-Luen Lee

Journal of Multivariate Analysis, 2005, vol. 92, issue 1, 77-96

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.

Keywords: Nonparametric; regression; Data-driven; smoothing; parameters; Autoregressive; process; Average; squared; error (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (5)

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