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| Adjusted R-squared (R²_adj)× | Bayesian Information Criterion (BIC)× | |
|---|---|---|
| Fachgebiet | Modellevaluation | Modellevaluation |
| Familie | MCDM | MCDM |
| Entstehungsjahr≠ | 1961 | 1978 |
| Urheber≠ | Henri Theil | Gideon E. Schwarz |
| Typ≠ | Penalized goodness-of-fit metric | Bayesian model selection metric |
| Wegweisende Quelle≠ | Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company. link ↗ | Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. DOI ↗ |
| Aliasnamen≠ | Adjusted R², R²_adj | BIC, Schwarz criterion, Schwarz information criterion |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predictor contributes meaningfully to explaining the target variable. | The Bayesian Information Criterion is an information-theoretic model selection criterion that approximates Bayesian model comparison. Introduced by Gideon Schwarz in 1978, BIC penalizes model complexity more heavily than AIC by using a sample-size-dependent penalty, making it particularly suitable for identifying the true underlying model structure. |
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