Wavelet-based estimators of mean regression function with long memory data

被引：0
|
作者：
Lin-yuan Li
Yi-min Xiao
机构：
[1] University of New Hampshire,Department of Mathematics and Statistics
[2] Michigan State University,Department of Statistics and Probability
来源：
Applied Mathematics and Mechanics | 2006年 / 27卷
关键词：
nonlinear wavelet-based estimator; nonparametric regression; long-range dependence; O212.7; 62G07; 62C20;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper provides an asymptotic expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators with long memory data. This MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous expansion for the kernel estimators. However, for the kernel estimators, this MISE expansion generally fails if the additional smoothness assumption is absent.
引用
收藏
页码：901 / 910
页数：9
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