Bandwidth selection for a data sharpening estimator in nonparametric regression

被引：3

作者：

Naito, Kanta ^{[1
]}

Yoshizaki, Masahiro ^{[1
]}

机构：

[1] Shimane Univ, Dept Math, Matsue, Shimane 6908504, Japan

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2009年 / 100卷 / 07期

基金：

日本学术振兴会;

关键词：

Bandwidth; Bias reduction; Data sharpening; Kernel; Nonparametric regression; Plug-in method; LEAST-SQUARES REGRESSION; BIAS REDUCTION METHOD; DENSITY-ESTIMATION;

D O I：

10.1016/j.jmva.2008.12.016

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：

This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator in nonparametric regression. Two kinds of bandwidths are considered: a bandwidth vector which has a different bandwidth for each covariate, and a scalar bandwidth that is common for all covariates. A plug-in method is developed and its theoretical performance is fully investigated. The proposed plug-in method works efficiently in our simulation study. (c) 2009 Elsevier Inc. All rights reserved.

引用

页码：1465 / 1486

页数：22

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