A comparison of multiple outlier detection methods for regression data
A comparison of multiple outlier detection methods for regression data
复制标题
DOI：
10.1080/03610910701812352
复制
发表时间：
2008-01-01
影响因子：
0.9
通讯作者：
Kiral, Gulsen
中科院分区：
文献类型：
--
作者：
Billor, Nedret;Kiral, Gulsen
统计数据中的异常值问题长期以来一直吸引着众多研究者的关注。因此，在统计文献中提出了许多离群值检测方法。然而，对于哪种方法总是优于其他方法，或者哪种方法推荐用于实际情况，还没有达成共识。在这篇文章中，我们进行了广泛的比较蒙特卡罗模拟研究，以评估性能的多个离群值检测方法，无论是最近提出的或经常引用的离群值检测文献。我们的模拟实验包括各种各样的现实和具有挑战性的回归场景。我们给出了在什么条件下哪种方法上级其他方法的建议。
The problem of outliers in statistical data has attracted many researchers for a long time. Consequently, numerous outlier detection methods have been proposed in the statistical literature. However, no consensus has emerged as to which method is uniformly better than the others or which one is recommended for use in practical situations. In this article, we perform an extensive comparative Monte Carlo simulation study to assess the performance of the multiple outlier detection methods that are either recently proposed or frequently cited in the outlier detection literature. Our simulation experiments include a wide variety of realistic and challenging regression scenarios. We give recommendations on which method is superior to others under what conditions.