Di Marzio, M, Fensore, S, Panzera, A et al. (1 more author) (2019) Kernel density classification for spherical data. Statistics and Probability Letters, 144. pp. 23-29. ISSN 0167-7152
Abstract
Classifying observations coming from two different spherical populations by using a nonparametric method appears to be an unexplored field, although clearly worth to pursue. We propose some decision rules based on spherical kernel density estimation and we provide asymptotic L₂ properties. A real-data application using global climate data is finally discussed.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. This is an author produced version of a paper published in Statistics & Probability Letters. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Classification; Directional data; Nonparametric methods |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Nov 2018 15:00 |
Last Modified: | 26 Jul 2019 00:43 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.spl.2018.07.018 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138925 |