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Mobius-like mappings and their use in kernel density estimation

Clements, Adam E., Hurn, Stanley, & Lindsay, Kenneth (2003) Mobius-like mappings and their use in kernel density estimation. Journal of the American Statistical Association, 98(464), pp. 993-1000.

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Abstract

It is well known that the manipulation of sample data by means of a parametric function can improve the performance of kernel density estimation. This article proposes a two-parameter Mobius-like function to map sample data drawn from a semi-infinite space into (-1,1). A standard kernel method is then used to estimate the density. The proposed method is shown to yield effective estimates of density and is computationally more efficient than other well-known transformation methods. The efficacy of the technique is demonstrated in a practical setting by application to two datasets.

Impact and interest:

13 citations in Scopus
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11 citations in Web of Science®

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ID Code: 8378
Item Type: Journal Article
Additional Information: For more information, please refer to the journal’s website (see hypertext link) or contact the author. Author contact details: a.clements@qut.edu.au
Keywords: ALGEBRAIC MAPPING, CURVATURE, INTEGRATED SQUARED ERROR, NONPARAMETRIC ESTIMATION
DOI: 10.1198/016214503000000945
ISSN: 0162-1459
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Copyright Owner: Copyright 2003 American Statistical Association
Deposited On: 02 Jul 2007
Last Modified: 23 Sep 2013 16:34

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