Bandwidth Choice for Average Derivative Estimation
作者:
W. Härdle,
J. Hart,
J.S. Marron,
AB. Tsybakov,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 417
页码: 218-226
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475195
出版商: Taylor & Francis Group
关键词: Bandwidth optimization;Kernel estimators
数据来源: Taylor
摘要:
The average derivative is the expected value of the derivative of a regression function. Kernel methods have been proposed as a means of estimating this quantity. The problem of bandwidth selection for these kernel estimators is addressed here. Asymptotic representations are found for the variance and squared bias. These are compared with each other to find an insightful representation for a bandwidth optimizing terms of lower order thann–1. It is interesting that, for dimensions greater than 1, negative kernels have to be used to prevent domination of bias terms in the asymptotic expression of the mean squared error. The extent to which the theoretical conclusions apply in practice is investigated in an economical example related to the so-called “law of demand.”
点击下载:
PDF (713KB)
返 回