On the Asymptotic Normality of an Estimate of a Regression Functional

被引：0

作者：

Gyoerfi, Laszlo ^{[1
]}

Walk, Harro ^{[2
]}

机构：

[1] Budapest Univ Technol & Econ, Dept Comp Sci & Informat Theory, Magyar Tudosok Korutja 2, H-1117 Budapest, Hungary

[2] Univ Stuttgart, Dept Math, D-70569 Stuttgart, Germany

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2015年 / 16卷

关键词：

nonparametric estimation; regression functional; central limit theorem; partitioning estimate;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An estimate of the second moment of the regression function is introduced. Its asymptotic normality is proved such that the asymptotic variance depends neither on the dimension of the observation vector, nor on the smoothness properties of the regression function. The asymptotic variance is given explicitly.

引用

页码：1863 / 1877

页数：15

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