Mobius-like mappings and their use in kernel density estimation
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.
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|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: email@example.com|
|Keywords:||ALGEBRAIC MAPPING, CURVATURE, INTEGRATED SQUARED ERROR, NONPARAMETRIC ESTIMATION|
|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:||21 Apr 2015 00:33|
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