Recursive local linear regression estimation and its applications

被引：4
|
作者：
Chen, Xing-Min [1 ]
Gao, Chao [2 ]
机构：
[1] School of Mathematical Sciences, Dalian University of Technology
[2] Beijing Institute of Information and Control
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2013年 / 30卷 / 04期
关键词：
Kernel estimation; Local linear regression; Nonlinear ARX systems; Recursive identification; Strong consistency;
D O I：
10.7641/CTA.2013.21076
中图分类号：
学科分类号：
摘要：
In nonparametric statistics, local polynomial regression is one of the most important tools. However, almost the previous work is based on nonrecursive algorithms. We investigate the recursive local linear regression estimation. The recursive algorithms are derived for the nonparametric estimation of the regression function and its derivative. Strong consistence of the estimates is established under reasonable conditions. The applications to estimation of the regression model with nonlinear conditional heteroskedasticity and identification of the nonlinear ARX (NARX) system are demonstrated by numerical simulation.
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页码：482 / 491
页数：9
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