NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE

被引：2

作者：

Zhao, Zhibiao ^{[1
]}

Zhang, Yiyun ^{[2
]}

Li, Runze ^{[1
]}

机构：

[1] Penn State Univ, University Pk, PA 16802 USA

[2] Novartis Oncol, E Hanover, NJ USA

来源：

JOURNAL OF TIME SERIES ANALYSIS | 2014年 / 35卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Differencing; long-range dependence; non-parametric regression; short-range dependence; time series; LONG-MEMORY; REGRESSION; MODELS;

D O I：

10.1111/jtsa.12044

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.

引用

页码：4 / 15

页数：12

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