A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Non-crossing convex quantile regression
Tekijät: Dai Sheng, Kuosmanen Timo, Zhou Xun
Kustantaja: Elsevier
Julkaisuvuosi: 2023
Journal: Economics Letters
Vuosikerta: 233
eISSN: 1873-7374
DOI: https://doi.org/10.1016/j.econlet.2023.111396
Verkko-osoite: https://doi.org/10.1016/j.econlet.2023.111396
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181304648
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.
Ladattava julkaisu This is an electronic reprint of the original article. |