论文标题
使用增量分位数估算Tukey深度
Estimating Tukey Depth Using Incremental Quantile Estimators
论文作者
论文摘要
深度的概念代表了测量任意点在数据集中的深度的方法，并且可以看作是偏爱的相反。事实证明，它非常有用，并根据概念开发了多种方法。 为了解决与深度概念相关的众所周知的计算挑战，我们建议使用最近开发的增量分位数估计器估算Tukey深度轮廓。提前已知数据集时，建议的算法可以估计深度轮廓，但也会递归更新，甚至跟踪Tukey Depth Counts，以动态变化的数据流分布。在现实数据示例中证明了跟踪，其中从加速度计观测值实时检测到人类活动的变化。
The concept of depth represents methods to measure how deep an arbitrary point is positioned in a dataset and can be seen as the opposite of outlyingness. It has proved very useful and a wide range of methods have been developed based on the concept. To address the well-known computational challenges associated with the depth concept, we suggest to estimate Tukey depth contours using recently developed incremental quantile estimators. The suggested algorithm can estimate depth contours when the dataset in known in advance, but also recursively update and even track Tukey depth contours for dynamically varying data stream distributions. Tracking was demonstrated in a real-life data example where changes in human activity was detected in real-time from accelerometer observations.
