发表机构
Renmin University of China（中国人民大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对区间数据提出一种基于接近度的统计深度方法，用于排序和异常值检测，并验证了其应用效果。
AI 中文摘要
随着技术的快速发展，区间数据因其能有效承载测量不确定性和原始变异性的信息，已在各个领域得到越来越广泛的应用。对区间数据进行合理的排序和异常值筛选对于探索其价值至关重要，而统计深度理论是实现这一目标的重要工具。现有的统计深度方法大多针对普通点值数据定义，难以适应区间数据的特性，无法满足其排序和异常值检测的需求。因此，本文提出了一种适用于区间数据的统计深度，并进行了相关的应用验证。
英文摘要
With the rapid development of technology, interval data have been increasingly widely used in various fields, as they can effectively carry information on measurement uncertainty and original variability. Reasonable ranking and outlier screening of interval data are crucial for exploring their value, and statistical depth theory is an important tool to achieve this goal. Existing statistical depth methods are mostly defined for ordinary point-valued data, which are difficult to adapt to the characteristics of interval data and cannot meet the needs of their ranking and outlier detection. Therefore, this paper proposes a statistical depth suitable for interval data and conducts relevant application verification.
Comments14 pages, 13 figures
