Abstract
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.
Original language | English |
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Pages (from-to) | 401-407 |
Number of pages | 7 |
Journal | Journal of the American Statistical Association |
Volume | 91 |
Issue number | 433 |
DOIs | |
State | Published - Mar 1 1996 |
Keywords
- Bandwidth selection
- Kernel density estimation
- Nonparametric curve estimation
- Smoothing parameter selection
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty