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Non-crossing convex quantile regression




TekijätDai Sheng, Kuosmanen Timo, Zhou Xun

KustantajaElsevier

Julkaisuvuosi2023

JournalEconomics Letters

Vuosikerta233

eISSN1873-7374

DOIhttps://doi.org/10.1016/j.econlet.2023.111396

Verkko-osoitehttps://doi.org/10.1016/j.econlet.2023.111396

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/181304648


Tiivistelmä

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.


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Last updated on 2025-27-03 at 21:58