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An efficient cross-validation algorithm for window width selection for nonparametric kernel regression

 

作者: Jeff Racine,  

 

期刊: Communications in Statistics - Simulation and Computation  （Taylor Available online 1993）
卷期: Volume 22, issue 4  

页码: 1107-1114

 

ISSN:0361-0918

 

年代: 1993

 

DOI:10.1080/03610919308813144

 

出版商: Marcel Dekker, Inc.

 

关键词: kernel regression;window width selection;cross-validation;computational efficiency

 

数据来源: Taylor

 

摘要:

This paper presents an approach to cross-validated window width choice which greatly reduces computation time, which can be used regardless of the nature of the kernel function, and which avoids the use of the Fast Fourier Transform. This approach is developed for window width selection in the context of kernel estimation of an unknown conditional mean.

 

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