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An iterative bandwidth selector for kernel estimation of densities and their derivatives
Authors:
Joachim Engel a;
Eva Herrmann b;
Theo Gasser c
| Affiliations: | a Institut f r Wirtschaftstheorie II, Universit t Bonn, Bonn, Germany |
| b Fachbereich Mathematik, Technische Hochschule Darmstadt, Darmstadt, Germany | |
| c Abteilung Biostatistik, ISPM, Universitt Zrich, Zrich, Switzerland |
DOI:
10.1080/10485259408832598
Publication Frequency:
8 issues per year
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
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PDF
(English)
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Abstract
A bandwidth selection rule which proved to be useful and effective for nonparametric kernel regression is modified to be suitable for estimation of a density and its derivatives. Various versions of the rule are considered. Theoretical properties are derived. A simulation study compares its finite-sample behavior with that of other bandwidth selectors.
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| Keywords: Bandwidth selection; density estimation; density derivatives; kernel estimators; plug-in method; smoothing |
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