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Using small bias nonparametric density estimators for confidence interval estimation

Di Marzio, M and Taylor, CC (2009) Using small bias nonparametric density estimators for confidence interval estimation. Journal of Nonparametric Statistics, 21 (2). 229 - 240 . ISSN 1048-5252


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Confidence intervals for densities built on the basis of standard nonparametric theory are doomed to have poor coverage rates due to bias. Studies on coverage improvement exist, but reasonably behaved interval estimators are needed. We explore the use of small bias kernel-based methods to construct confidence intervals, in particular using a geometric density estimator that seems better suited for this purpose.

Item Type: Article
Copyright, Publisher and Additional Information: © 2009 Taylor & Francis. This is an author produced version of a paper subsequently published in Journal of Nonparametric Statistics. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: bootstrap, coverage rate, geometric density estimators, higher-order bias estimators, U-statistic
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 09 May 2011 09:33
Last Modified: 15 Sep 2014 04:00
Published Version: http://dx.doi.org/10.1080/10485250802562607
Publisher: Taylor & Francis Ltd
Identification Number: 10.1080/10485250802562607
URI: http://eprints.whiterose.ac.uk/id/eprint/42950

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