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

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 expansio...

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Bibliographic Details
Published in:Applied mathematics and mechanics 2006-07, Vol.27 (7), p.901-910
Main Author: 李林元 肖益民
Format: Article
Language:English
Subjects:
Asymptotic expansions
Errors
Estimators
Kernels
Mathematical analysis
Nonlinearity
Regression
Smoothness
存储数据
渐近扩展
非参数衰退
非线性小波估计器
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Summary: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.
ISSN:0253-4827
1573-2754
DOI:10.1007/s10483-006-0705-1