Non-crossing convex quantile regression
Dai Sheng; Kuosmanen Timo; Zhou Xun
Non-crossing convex quantile regression
Dai Sheng
Kuosmanen Timo
Zhou Xun
arXiv
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022081154196
https://urn.fi/URN:NBN:fi-fe2022081154196
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]