Nearest neighbor estimates of regression

被引：1
|
作者：
Doksum, Kjell A. [1 ]
Jiang, Jiancheng [2 ]
Sun, Bo [3 ,4 ]
Wang, Shuzhen [5 ]
机构：
[1] Univ Wisconsin Madison, Madison, WI 53706 USA
[2] Univ N Carolina, Charlotte, NC 28223 USA
[3] Wuhan Univ, Wuhan 430072, Hubei, Peoples R China
[4] Jishou Univ, Jishou 416000, Hunan, Peoples R China
[5] Beijing Union Univ, Coll Business, Beijing 100101, Peoples R China
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2017年 / 110卷
关键词：
Empirical plug-in estimation; Local polynomial; Boundary adaptive; Minimax efficiency; WAVELET SHRINKAGE; ADDITIVE-MODELS; SMOOTHERS; SELECTION; SPLINES;
D O I：
10.1016/j.csda.2016.12.014
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
New nearest neighbor estimators of the nonparametric regression function and its derivatives are developed. Asymptotic normality is obtained for the proposed estimators over the interior points and the boundary region. Connections with other estimators such as local polynomial smoothers are established. The proposed estimators are boundary adaptive and extensions of the Stute estimators. Asymptotic minimax risk properties are also established for the proposed estimators. Simulations are conducted to compare the performance of the proposed estimators with others. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：64 / 74
页数：11
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