Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Faktor Inflasi Varians (VIF)× | Indeks Keadaan× | |
|---|---|---|
| Bidang | Ekonometrik | Ekonometrik |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1970 | 1980 |
| Pengasas≠ | Donald Marquardt | Belsley, Kuh & Welsch |
| Jenis≠ | Diagnostic statistic | Collinearity diagnostic index |
| Sumber perintis≠ | Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612. DOI ↗ | 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 |
| Alias | VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü | Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi |
| Berkaitan≠ | 3 | 2 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
|
|