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
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 May 2011 09:33 |
Last Modified: | 18 Apr 2017 05:15 |
Published Version: | http://dx.doi.org/10.1080/10485250802562607 |
Publisher: | Taylor & Francis Ltd |
Identification Number: | 10.1080/10485250802562607 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:42950 |