Dai, Sheng, Kuosmanen, Timo and Zhou, Xun orcid.org/0000-0003-2093-4508 (2023) Non-crossing convex quantile regression. Economics Letters. 111396. ISSN 0165-1765
Abstract
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A direct approach to address this problem is to impose non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s) |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Environment and Geography (York) |
Depositing User: | Pure (York) |
Date Deposited: | 17 Oct 2023 15:40 |
Last Modified: | 16 Oct 2024 19:31 |
Published Version: | https://doi.org/10.1016/j.econlet.2023.111396 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.econlet.2023.111396 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204316 |
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