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Lookup NU author(s): Dr Daniel Henderson
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We consider kernel-type methods for the estimation of a density on 0,1 which eschew explicit boundary correction. We propose using kernels that are symmetric in their two arguments; these kernels are conditional densities of bivariate copulas. We give asymptotic theory for the version of the new estimator using Gaussian copula kernels and report on simulation comparisons of it with the beta-kernel density estimator of Chen ([1]). We also provide automatic bandwidth selection in the form of 'rule-of-thumb' bandwidths for both estimators. As well as its competitive integrated squared error performance, advantages of the new approach include its greater range of possible values at 0 and 1, the fact that it is a bona fide density and that the individual kernels and resulting estimator are comprehensible in terms of a single simple picture. © 2007 Biometrika Trust.
Author(s): Jones MC, Henderson DA
Publication type: Article
Publication status: Published
Journal: Biometrika
Year: 2007
Volume: 94
Issue: 4
Pages: 977-984
ISSN (print): 0006-3444
ISSN (electronic): 1464-3510
Publisher: Oxford University Press
URL: http://dx.doi.org/10.1093/biomet/asm068
DOI: 10.1093/biomet/asm068
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