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在具有指数和帕雷托尾巴的样本中检测到多个异常值
1Institute of Risk Analysis, Prediction and Management, Southern University of Science and Technology, Shenzhen, People's Republic of China.
Journal of applied statistics
|February 6, 2026
概括
我们开发了新的强大的统计数据 (MRS和SRS) 用于在重尾数据中检测异常值. 这些方法提高了对遮和浸泡的抵抗力,使得连续测试再次具有竞争力.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
背景情况:
- 异常值的检测对于分析重尾数据 (例如指数,帕雷托) 至关重要.
- 传统的方法难以掩盖和浸泡,特别是在连续测试中.
- 由于这些局限性,内向测试已经被外向测试所掩盖.
研究的目的:
- 介绍新的稳定性测试统计,最大稳定性和 (MRS) 和总结-稳定性和 (SRS).
- 在具有指数或帕雷托尾巴的样本中提高异常值检测的稳定性.
- 重新建立内向连续测试作为外向测试的可行替代方案.
主要方法:
- 开发了两个基于比率的可靠测试统计:MRS和SRS.
- 统计数据将最大的异常值与削减的部分样本总和进行比较.
- 导出分析的零分布,并进行模拟以进行性能比较.
主要成果:
- MRS和SRS表现出更好的抗掩蔽和浴的抵抗力.
- 拟议的测试显著减少了向内顺序测试中的掩盖问题.
- 对各种数据场景的经典统计数据进行了绩效评估.
结论:
- 在重尾分布中,MRS和SRS提供了一个强大的方法来检测异常值.
- 内向连续测试被重新确立为一种无需多次测试校正的竞争性方法.
- "龙王"概念在各种案例研究中应用于显著的异常值.
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