论文标题
非交叉凸射击回归
Non-crossing convex quantile regression
论文作者
论文摘要
分位数交叉是形状约束非参数分位回归的常见现象。 Wang等人最近的一项研究。 （2014年）提议通过对凸射击回归施加非交叉约束来解决此问题。但是，非交叉约束可能违反固有的分位特性。本文提出了一种惩罚的凸射击回归方法，该方法可以绕过分位数交叉，同时更好地维护分解性质。一项蒙特卡洛研究证明了拟议的惩罚方法在解决分数交叉问题时的优越性。
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.
