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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|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 00:00|
|Last Modified:||21 Apr 2015 00:33|
Repository Staff Only: item control page