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

页码：199 / 205

页数：7

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