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| SCAD регуляризация с наказателна функция× | Редундантен анализ× | |
|---|---|---|
| Област | Психометрия | Психометрия |
| Семейство | Latent structure | Latent structure |
| Година на възникване≠ | 2001 | 1977 |
| Създател≠ | Jianqing Fan, Runze Li | Albert van den Wollenberg |
| Тип≠ | Penalized regression with non-concave penalty | Asymmetric multivariate analysis |
| Основополагащ източник≠ | Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360. DOI ↗ | van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219. DOI ↗ |
| Други названия | SCAD | RDA |
| Свързани | 5 | 5 |
| Резюме≠ | SCAD (Smoothly Clipped Absolute Deviation) is a variable selection and regularization method developed by Fan and Li (2001) that addresses limitations of L1 penalization (lasso). SCAD uses a non-concave penalty that automatically performs variable selection while maintaining oracle properties: it recovers the true underlying model as if the true predictors were known in advance. | Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variation in response variables, making it ideal for understanding how sets of predictors collectively explain multivariate outcomes. |
| ScholarGateНабор от данни ↗ |
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