Nonparametric M-estimation for Functional Stationary Ergodic Data

被引：0
|
作者：
Xian-zhu Xiong
Zheng-yan Lin
机构：
[1] Fuzhou University,College of Mathematics and Computer Science
[2] Zhejiang University,Department of Mathematics
来源：
Acta Mathematicae Applicatae Sinica, English Series | 2019年 / 35卷
关键词：
nonparametric M-estimator; functional stationary ergodic data; weak consistency; asymptotic normality; 62G05; 62G08; 62G20;
D O I：
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中图分类号：
学科分类号：
摘要：
This paper considers a nonparametric M-estimator of a regression function for functional stationary ergodic data. More precisely, in the ergodic data setting, we consider the regression of a real random variable Y over an explanatory random variable X taking values in some semi-metric abstract space. Under some mild conditions, the weak consistency and the asymptotic normality of the M-estimator are established. Furthermore, a simulated example is provided to examine the finite sample performance of the M-estimator.
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页码：491 / 512
页数：21
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