Skip Navigation

Biometrika 2007 94(4):977-984; doi:10.1093/biomet/asm068
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Jones, M.C.
Right arrow Articles by Henderson, D.A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2007 Biometrika Trust

Miscellanea

Miscellanea Kernel-Type Density Estimation on the Unit Interval

M.C. Jones

Department of Statistics, The Open University, Walton Hall, Milton Keynes MK7 6AA, U.K. m.c.jones{at}open.ac.uk

D.A. Henderson

School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K. d.a.henderson{at}ncl.ac.uk

Received for publication 1 December 2006. Revision received 1 March 2007.
  Abstract

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.

Key Words: Boundary behaviour • Copula • Kernel density estimation


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.