Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Regressió amb penalització SCAD× | Modelització d'equacions estructurals per a mínims quadrats parcials× | |
|---|---|---|
| Camp | Psicometria | Psicometria |
| Família | Latent structure | Latent structure |
| Any d'origen≠ | 2001 | 1985 |
| Autor original≠ | Jianqing Fan, Runze Li | Herman Wold |
| Tipus≠ | Penalized regression with non-concave penalty | Component-based structural equation model |
| Font seminal≠ | 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 |
| Àlies≠ | SCAD | PLS-SEM, PLS path modeling |
| Relacionats | 5 | 5 |
| Resum≠ | 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. |
| ScholarGateConjunt de dades ↗ |
|
|