On bandwidth choice in nonparametric regression with both short- and long-range dependent errors

• 成果类型: Article
• 署名作者: Hall, P; Lahiri, SN; Polzehl, J
• 署名单位: Zuse Institute Berlin; Iowa State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1995
• 页码: 1921-1936
• 关键词: DENSITY-ESTIMATION; bootstrap choice; kernel

摘要

We analyse methods based on the block bootstrap and leave-out cross-validation, for choosing the bandwidth in nonparametric regression when errors have an almost arbitrarily long range of dependence. A novel analytical device for modelling the dependence structure of errors is introduced. This allows a concise theoretical description of the way in which the range of dependence affects optimal bandwidth choice. It is shown that, provided block length or leave-out number, respectively, are chosen appropriately, both techniques produce first-order optimal bandwidths. Nevertheless, the block bootstrap has far better empirical properties, particularly under long-range dependence.