RENORMALIZATION AND WHITE-NOISE APPROXIMATION FOR NONPARAMETRIC FUNCTIONAL ESTIMATION PROBLEMS

• 成果类型: Article
• 署名作者: LOW, MG
• 署名单位: University of California System; University of California Berkeley
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348538
• 发表日期: 1992
• 页码: 545-554
• 关键词: convergence

摘要

White noise models often renormalize exactly yielding optimal rates of convergence for pointwise nonparametric functional estimation problems. Similar rescaling ideas lead to a sequence of experiments appropriate for pointwise density estimation problems.