Di Marzio, M, Fensore, S, Panzera, A et al. (1 more author) (2019) Local binary regression with spherical predictors. Statistics and Probability Letters, 144. pp. 30-36. ISSN 0167-7152
Abstract
We discuss local regression estimators when the predictor lies on the -dimensional sphere and the response is binary. Despite Di Marzio et al. (2018b), who introduce spherical kernel density classification, we build on the theory of local polynomial regression and local likelihood. Simulations and a real-data application illustrate the effectiveness of the proposals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. This is an author produced version of a paper published in Statistics and Probability Letters. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Local likelihood; Spherical kernels; Tangent-normal decomposition |
Dates: |
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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 15:48 |
Last Modified: | 26 Jul 2019 00:43 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.spl.2018.07.019 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138926 |