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arXiv · 2204.01371

Non-crossing convex quantile regression

Sheng Dai·Timo Kuosmanen·Xun Zhou
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Abstract

Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing 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 better 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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Sheng Dai, Timo Kuosmanen, Xun Zhou. 2022-04-04. Non-crossing convex quantile regression. https://doi.org/10.1016/j.econlet.2023.111396

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