Generalized least squares cross‐validation in kernel density estimation
The kernel density estimation is a popular method in density estimation. The main issue is bandwidth selection, which is a well‐known topic and is still frustrating statisticians. A robust least squares cross‐validation bandwidth is proposed, which significantly improves the classical least squares cross‐validation bandwidth for its variability and undersmoothing, adapts to different kinds of densities, and outperforms the existing bandwidths in statistical literature and software.
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Dokumenttyp: | Artikel |
Reihe/Periodikum: | Statistica Neerlandica |
Verlag/Hrsg.: |
Oxford,
Blackwell
|
Sprache: | Englisch |
ISSN: | 0039-0402 |
Weitere Identifikatoren: | doi: 10.1111/stan.12061 |
Permalink: | https://search.fid-benelux.de/Record/olc-benelux-1964987520 |
URL: | NULL NULL |
Datenquelle: | Online Contents Benelux; Originalkatalog |
Powered By: | Verbundzentrale des GBV (VZG) |
Link(s) : | http://dx.doi.org/10.1111/stan.12061
http://dx.doi.org/10.1111/stan.12061 |
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