Machine learningMachine learning
稳健线性回归
稳健线性回归在预测变量与连续结果变量之间拟合线性模型，同时降低或剔除有影响力的异常值，从而防止少数异常观测值（普通最小二乘法以其敏感性而闻名）扭曲整个估计直线。主要变体包括 Huber 回归、迭代重加权最小二乘法 (IRLS)、RANSAC 和 Theil-Sen 估计。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI: 10.1214/aoms/1177703732 ↗
- Rousseeuw, P. J. & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley. ISBN: 978-0-471-85233-9
如何引用本页
ScholarGate. (2026, June 3). Robust Linear Regression (Outlier-Resistant Estimation). ScholarGate. https://scholargate.app/zh/machine-learning/robust-linear-regression
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.
- Huber回归统计学↔ compare
- Lasso 回归机器学习↔ compare
- 线性回归 (ML)机器学习↔ compare
- 分位数回归计量经济学↔ compare
- 正则化线性回归机器学习↔ compare