An adaptation theory for nonparametric confidence intervals

• 成果类型: Article
• 署名作者: Cai, TT; Low, MG
• 署名单位: University of Pennsylvania
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/00905360400000049
• 发表日期: 2004
• 页码: 1805-1840
• 关键词: regression; rates; sets

摘要

A nonparametric adaptation theory is developed for the construction of confidence intervals for linear functionals. A between class modulus of continuity captures the expected length of adaptive confidence intervals. Sharp lower bounds are given for the expected length and an ordered modulus of continuity is used to construct adaptive confidence procedures which are within a constant factor of the lower bounds. In addition, minimax theory over nonconvex parameter spaces is developed.