Di Marzio, M., Fensore, S. orcid.org/0000-0002-5308-2586 and Taylor, C.C. orcid.org/0000-0003-0181-1094 (2023) Kernel regression for errors-in-variables problems in the circular domain. Statistical Methods & Applications, 32 (4). pp. 1217-1237. ISSN 1618-2510
Abstract
We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of measurement errors. We propose estimators whose weight functions are deconvolution kernels defined according to the nature of the involved variables. We derive the asymptotic properties of the proposed estimators and consider possible generalizations and extensions. We provide some simulation results and a real data case study to illustrate and compare the proposed methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Characteristic function; Deconvolution kernels; Fourier coefficients; Measurement errors; Wind direction; CO pollution |
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: | 04 Jan 2024 17:43 |
Last Modified: | 04 Jan 2024 17:43 |
Published Version: | http://dx.doi.org/10.1007/s10260-023-00687-0 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Identification Number: | 10.1007/s10260-023-00687-0 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206369 |