Asymptotic properties of nonparametric M-estimation for mixing functional data

被引：13

作者：

Chen, Jia ^{[1
]}

Zhang, Lixin ^{[1
]}

机构：

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

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2009年 / 139卷 / 02期

基金：

中国国家自然科学基金;

关键词：

alpha-Mixing; Asymptotic normality; Consistency; Functional data; Nonparametric M-estimation; TIME-SERIES; REGRESSION ESTIMATION; LOCATION; MODELS;

D O I：

10.1016/j.jspi.2008.05.007

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：

We investigate the asymptotic behavior of a nonparametric M-estimator of a regression function for stationary dependent processes, where the explanatory variables take values in some abstract functional space. Under some regularity conditions, we give the weak and strong consistency of the estimator as well as its asymptotic normality. We also give two examples of functional processes that satisfy the mixing conditions assumed in this paper. Furthermore, a simulated example is presented to examine the finite sample performance of the proposed estimator. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：533 / 546

页数：14

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