M-estimation in nonparametric regression under strong dependence and infinite variance

被引：3
|
作者：
Chan, Ngai Hang [1 ]
Zhang, Rongmao [2 ]
机构：
[1] Chinese Univ Hong Kong, Dept Stat, Shatin, NT, Peoples R China
[2] Zhejiang Univ, Dept Math, Hangzhou 310027, Peoples R China
来源：
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2009年 / 61卷 / 02期
关键词：
Heavy-tailed; Long-range dependence; M-estimation; Nonparametric regression; Stable distribution; LONG-RANGE DEPENDENCE; MOVING AVERAGES; EMPIRICAL PROCESSES; ROBUST REGRESSION; MEMORY ERRORS; ASYMPTOTICS; BANDWIDTH; RATES;
D O I：
10.1007/s10463-007-0142-4
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：
A robust local linear regression smoothing estimator for a nonparametric regression model with heavy-tailed dependent errors is considered in this paper. Under certain regularity conditions, the weak consistency and asymptotic distribution of the proposed estimators are obtained. If the errors are short-range dependent, then the limiting distribution of the estimator is normal. If the data are long-range dependent, then the limiting distribution of the estimator is a stable distribution.
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页码：391 / 411
页数：21
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