Regression model
S估计量稳健回归
S估计量是一种稳健线性回归方法，由Rousseeuw和Yohai于1984年提出，它通过最小化残差尺度的稳健M估计量来估计系数，而不是最小化残差的方差。通过降低残差离散度的有界度量，它可以达到高达50%的崩溃点，因此即使在大量数据是异常值的情况下也能保持可靠性，并且它是著名MM估计量的第一阶段。
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来源
- Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI: 10.1007/978-1-4615-7821-5_15 ↗
- Maronna, R. A., Martin, R. D., Yohai, V. J. & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687
如何引用本页
ScholarGate. (2026, June 1). S-Estimator for Robust Regression. ScholarGate. https://scholargate.app/zh/statistics/s-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.
- MM估计量稳健回归统计学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 分位数回归计量经济学↔ compare
- 回归的Tau (τ)估计量统计学↔ compare
- Theil-Sen 估计器统计学↔ compare