A brief survey of bandwidth selection for density estimation

• 成果类型: Article
• 署名作者: Jones, MC; Marron, JS; Sheather, SJ
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of New South Wales Sydney
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291420
• 发表日期: 1996
• 页码: 401-407
• 关键词: data-based algorithm; Cross-validation; bootstrap choice; window width; kernel; derivatives; error; point

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.