RENORMALIZATION AND WHITE-NOISE APPROXIMATION FOR NONPARAMETRIC FUNCTIONAL ESTIMATION PROBLEMS

被引：17

作者：

LOW, MG ^{[1
]}

机构：

[1] UNIV CALIF BERKELEY, BERKELEY, CA 94720 USA

来源：

ANNALS OF STATISTICS | 1992年 / 20卷 / 01期

关键词：

NONPARAMETRIC FUNCTIONAL ESTIMATION; RENORMALIZATION; WHITE NOISE APPROXIMATION; DENSITY ESTIMATION;

D O I：

10.1214/aos/1176348538

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：545 / 554

页数：10

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