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| Bedingungsindex× | Ridge Regression× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Maschinelles Lernen |
| Familie≠ | Regression model | Machine learning |
| Entstehungsjahr≠ | 1980 | 1970 |
| Urheber≠ | Belsley, Kuh & Welsch | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Collinearity diagnostic index | L2-regularized linear regression |
| Wegweisende Quelle≠ | Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons. ISBN: 978-0-471-05856-4 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Aliasnamen | Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwandt≠ | 2 | 4 |
| Zusammenfassung≠ | The Condition Index, introduced by Belsley, Kuh, and Welsch (1980), is a scalar measure derived from singular value decomposition of the scaled regressor matrix. It quantifies the degree of near-linear dependence among predictors in ordinary least squares regression, enabling analysts to detect collinearity that inflates coefficient variance and destabilises parameter estimates. Widely used in economics, social sciences, and biomedical research wherever OLS regression is applied. | 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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