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| 方差膨胀因子 (VIF)× | 岭回归（Ridge Regression）× | |
|---|---|---|
| 领域≠ | 计量经济学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份 | 1970 | 1970 |
| 提出者≠ | Donald Marquardt | Hoerl, A.E. & Kennard, R.W. |
| 类型≠ | Diagnostic statistic | L2-regularized linear regression |
| 开创性文献≠ | Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| 别名 | VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 相关≠ | 3 | 4 |
| 摘要≠ | The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in econometrics, social science, and biomedical research whenever analysts suspect that two or more independent variables move together closely enough to destabilize coefficient estimates. | 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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