Nonparametric M-estimation with long-memory errors

被引：6
作者：
Beran, J
Ghosh, S
Sibbertsen, P
机构：
[1] Univ Konstanz, Dept Math & Stat, D-78457 Constance, Germany
[2] Univ Dortmund, Dept Stat, Dortmund, Germany
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2003年 / 117卷 / 02期
关键词：
CONVERGENCE; REGRESSION;
D O I：
10.1016/S0378-3758(02)00391-9
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 behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example. (C) 2002 Elsevier B.V. All rights reserved.
引用
收藏
页码：199 / 205
页数：7
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