Mobius-like mappings and their use in kernel density estimation

Clements, A.E., Hurn, A.S. and Lindsay, K.A. (2003) Mobius-like mappings and their use in kernel density estimation. Journal of the American Statistical Association, 98(464), pp. 993-1000. (doi: 10.1198/016214503000000945)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lindsay, Professor Kenneth
• Authors: Clements, A.E., Hurn, A.S., and Lindsay, K.A.
• Subjects: Q Science > QA Mathematics
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Mathematics
• Journal Name: Journal of the American Statistical Association
• ISSN: 0162-1459
• ISSN (Online): 1537-274X