Optimal rates of convergence for nonparametric regression estimation under anisotropic Holder condition

被引：0

作者：

Guo, Huijun ^{[1
]}

Kou, Junke ^{[1
]}

机构：

[1] Guilin Univ Elect Technol, Sch Math & Computat Sci, Guilin, Guangxi, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2024年 / 53卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Regression estimation; convergence rate; optimality; anisotropic Holder condition;

D O I：

10.1080/03610926.2022.2091781

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：

In the multidimensional setting, we consider the nonparametric regression estimation with errors-in-variables. Both ordinary smooth noise and super smooth one are assumed for errors in the covariates. An anisotropic kernel estimator is provided based on a deconvolution technique. We study the pointwise estimation and obtain the optimal rates of convergence under the anisotropic Holder condition.

引用

页码：687 / 699

页数：13

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