Robust nonparametric kernel regression estimator

被引：18
|
作者：
Zhao, Ge [1 ]
Ma, Yanyuan [1 ]
机构：
[1] Univ S Carolina, Columbia, SC 29208 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2016年 / 116卷
关键词：
Kernel; Nonparametric regression; Outliers; Robust; Smoothing; BANDWIDTH SELECTION; MODELS;
D O I：
10.1016/j.spl.2016.04.010
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 robust nonparametric kernel regression context, we prescribe method to select trimming parameter and bandwidth. Through solving estimating equations, we control outlier effect through combining weighting and trimming. We show asymptotic consistency, establish bias, variance properties and derive asymptotics. (C) 2016 Elsevier B.V. All rights reserved.
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页码：72 / 79
页数：8
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