WAVELET-BASED ESTIMATORS OF MEAN REGRESSION FUNCTION WITH LONG MEMORY DATA

被引：0

作者：

李林元 ^{[1
]}

肖益民 ^{[2
]}

机构：

[1] Department of Mathematics and Statistics University of New Hampshire, 03824, USA

[2] Department of Statistics and Probability Michigan State University, 48824, USA

来源：

Applied Mathematics and Mechanics(English Edition) | 2006年 / 07期

关键词：

nonlinear wavelet-based estimator; nonparametric regression; long-range dependence;

D O I：

暂无

中图分类号：

O212 [数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

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

页码：901 / 910

页数：10

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