Nonparametric M-estimation for Functional Stationary Ergodic Data

被引：2

作者：

Xiong, Xian-zhu ^{[1
]}

Lin, Zheng-yan ^{[2
]}

机构：

[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Fujian, Peoples R China

[2] Zhejiang Univ, Dept Math, Hangzhou 310027, Zhejiang, Peoples R China

来源：

ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES | 2019年 / 35卷 / 03期

基金：

中国国家自然科学基金;

关键词：

nonparametric M-estimator; functional stationary ergodic data; weak consistency; asymptotic normality; REGRESSION ESTIMATION; TIME-SERIES; ASYMPTOTIC NORMALITY; ROBUST ESTIMATOR; MODEL; INFERENCE;

D O I：

10.1007/s10255-019-0826-6

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

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.

引用

页码：491 / 512

页数：22

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