Regression model
MM估计量稳健回归
MM估计量是Victor J. Yohai于1987年提出的一种稳健线性回归方法。它结合了S估计量的高崩溃点和M估计量的高效率，因此它能强烈抵抗异常值，同时在误差表现良好的情况下仍能高效地利用数据。
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来源
- Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI: 10.1214/aos/1176350366 ↗
- Koller, M. & Stahel, W. A. (2011). Sharpening Wald-type Inference in Robust Regression for Small Samples. Computational Statistics & Data Analysis, 55(8), 2504-2515. DOI: 10.1016/j.csda.2011.02.014 ↗
如何引用本页
ScholarGate. (2026, June 1). MM-Estimation for Robust Regression. ScholarGate. https://scholargate.app/zh/statistics/mm-estimator
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.
- 最小中位数平方（LMS）回归统计学↔ compare
- 最小裁剪平方和（LTS）回归统计学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- RANSAC回归统计学↔ compare
- Theil-Sen 估计器统计学↔ compare