A Brief Survey of Bandwidth Selection for Density Estimation
作者:
M.C. Jones,
J.S. Marron,
S.J. Sheather,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 433
页码: 401-407
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476701
出版商: Taylor & Francis Group
关键词: Bandwidth selection;Kernel density estimation;Nonparametric curve estimation;Smoothing parameter selection
数据来源: Taylor
摘要:
There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts.
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