Regression model
稳健的马氏距离
稳健的马氏距离通过测量每个观测值与数据中心点的距离来识别多元异常值，该距离使用稳健的协方差估计量。它建立在 Rousseeuw 和 Van Zomeren (1990) 的稳健距离框架以及 Filzmoser、Garrett 和 Reimann (2005) 的多元异常值检测方法之上，用最小协方差行列式 (MCD) 估计量替换经典的均值和协方差，从而使异常值本身不会扭曲距离。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Rousseeuw, P. J. & Van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633-639. DOI: 10.1080/01621459.1990.10474920 ↗
- Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate Outlier Detection in Exploration Geochemistry. Computational Statistics & Data Analysis, 49(2), 561-587. DOI: 10.1016/j.cageo.2004.11.013 ↗
如何引用本页
ScholarGate. (2026, June 1). Robust Mahalanobis Distance (MCD-based Multivariate Outlier Detection). ScholarGate. https://scholargate.app/zh/statistics/mahalanobis-robust
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 经调整的箱线图（Adjusted Boxplot）统计学↔ compare
- 最小裁剪平方和（LTS）回归统计学↔ compare
- 中位数绝对离差 (MAD) 估计统计学↔ compare
- 稳健方差分析（Welch & Trimmed Mean）统计学↔ compare
- Theil-Sen 估计器统计学↔ compare