Comparer des méthodes

	Régression pénalisée SCAD	Modélisation par équations structurelles par moindres carrés partiels
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2001	1985
Auteur d'origine	Jianqing Fan, Runze Li	Herman Wold
Type	Penalized regression with non-concave penalty	Component-based structural equation model
Source fondatrice	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.	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
Alias	SCAD	PLS-SEM, PLS path modeling
Apparentées	5	5
Résumé	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.