Prequential and Cross-Validated Regression Estimation
Machine Learning
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Smoothing survival densities in practice
Computational Statistics & Data Analysis
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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.