Adaptive Bandwidth Choice for Kernel Regression
作者:
WilliamR. Schucany,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 430
页码: 535-540
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476545
出版商: Taylor & Francis Group
关键词: Curve estimation;Local bandwidth selection;Local linear;Nonparametric regression;Smoothing
数据来源: Taylor
摘要:
A data-based procedure is introduced for local bandwidth selection for kernel estimation of a regression function at a point. The estimated bandwidth is shown to be consistent and asymptotically normal as an estimator of the (asymptotic) optimal value for minimum mean square estimation. Simulation studies indicate satisfactory behavior of the new bandwidth estimator in finite samples. The findings are improvements over a global bandwidth estimator. The same methodology works for local linear regression and extends easily to weighted local polynomial fits.
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