RENORMALIZATION EXPONENTS AND OPTIMAL POINTWISE RATES OF CONVERGENCE

• 成果类型: Article
• 署名作者: DONOHO, DL; LOW, MG
• 署名单位: University of Pennsylvania
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348665
• 发表日期: 1992
• 页码: 944-970
• 关键词: DENSITY-ESTIMATION; regression; kernel; RISK

摘要

Simple renormalization arguments can often be used to calculate optimal rates of convergence for estimating linear functionals from indirect measurements contaminated with white noise. This allows one to quickly identify optimal rates for certain problems of density estimation, nonparametric regression, signal recovery and tomography. Optimal kernels may also be derived from renormalization; we give examples for deconvolution and tomography.