Compară metode

	Factor de Inflație a Varianței (VIF)	Indexul de Condiție	Regresia Ridge
Domeniu	Econometrie	Econometrie	Învățare automată
Familie	Regression model	Regression model	Machine learning
Anul apariției	1970	1980	1970
Autorul original	Donald Marquardt	Belsley, Kuh & Welsch	Hoerl, A.E. & Kennard, R.W.
Tip	Diagnostic statistic	Collinearity diagnostic index	L2-regularized linear regression
Sursa seminală	Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612.	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.
Denumiri alternative	VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü	Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Înrudite	3	2	4
Rezumat	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.	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.