COMPARISON OF 2 BANDWIDTH SELECTORS WITH DEPENDENT ERRORS

• 成果类型: Article
• 署名作者: CHU, CK; MARRON, JS
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348377
• 发表日期: 1991
• 页码: 1906-1918
• 关键词: regression

摘要

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.