论文标题
定向分位数分类器
Directional quantile classifiers
论文作者
论文摘要
我们根据方向分位数介绍分类器。我们得出了在给定方向下选择最佳分位水平的理论结果，相反，在给定分数水平的情况下，我们是最佳方向。我们还表明，如果人口分布最多有所不同，并且如果允许方向数量以相同的速度差异，则错误分类率是无限的。我们通过仿真研究和真实的数据示例说明了我们提出的分类器在小维度和高维度设置中的令人满意的性能。实施该方法的代码在R软件包Qtools中公开可用。
We introduce classifiers based on directional quantiles. We derive theoretical results for selecting optimal quantile levels given a direction, and, conversely, an optimal direction given a quantile level. We also show that the misclassification rate is infinitesimal if population distributions differ by at most a location shift and if the number of directions is allowed to diverge at the same rate of the problem's dimension. We illustrate the satisfactory performance of our proposed classifiers in both small and high dimensional settings via a simulation study and a real data example. The code implementing the proposed methods is publicly available in the R package Qtools.
