Linganisha mbinu

	Usawazishaji wa Regressi kwa Adhabu ya SCAD	Uchanganuzi wa Urejeshi
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	2001	1977
Mwanzilishi	Jianqing Fan, Runze Li	Albert van den Wollenberg
Aina	Penalized regression with non-concave penalty	Asymmetric multivariate analysis
Chanzo asilia	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.	van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219.
Majina mbadala	SCAD	RDA
Zinazohusiana	5	5
Muhtasari	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.