Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Penalizovaná regrese SCAD× | Parciální metoda nejmenších čtverců v modelování pomocí strukturálních rovnic× | |
|---|---|---|
| Obor | Psychometrika | Psychometrika |
| Rodina | Latent structure | Latent structure |
| Rok vzniku≠ | 2001 | 1985 |
| Tvůrce≠ | Jianqing Fan, Runze Li | Herman Wold |
| Typ≠ | Penalized regression with non-concave penalty | Component-based structural equation model |
| Původní zdroj≠ | 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 ↗ | Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445 |
| Další názvy≠ | SCAD | PLS-SEM, PLS path modeling |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data. |
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