方法对比
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| 稳健多元线性回归× | 岭回归（Ridge Regression）× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 1964–1980s | 1970 |
| 提出者≠ | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna | Hoerl, A.E. & Kennard, R.W. |
| 类型≠ | Robust linear regression | L2-regularized linear regression |
| 开创性文献≠ | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| 别名 | robust MLR, M-estimator regression, resistant multiple regression, robust OLS | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 相关≠ | 6 | 4 |
| 摘要≠ | Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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