Comparison of Two Bandwidth Selectors with Dependent Errors
The Annals of Statistics · 1991 · Volume 19 · Issue 4
Abstract
For nonparametric regression, in the case of dependent observations, cross-validation is known to be severely affected by dependence. This effect is precisely quantified through a limiting distribution for the cross-validated bandwidth. The performance of two methods, the "leave-$(2l + 1)$-out" version of cross-validation and partitioned cross-validation, which adjust for the effect of dependence on bandwidth selection is investigated. The bandwidths produced by these two methods are analyzed by further limiting distributions which reveal significantly different characteristics. Simulations demonstrate that the asymptotic effects hold for reasonable sample sizes.
Keywords
62G05; 62G20; autoregressive moving average process; bandwidth selector; cross-validation; Nonparametric regression; partitioned cross-validation;
Enable JavaScript for citation tools and additional paper interactions.