Usporedite metode

	Regresija s SCAD penalizacijom	MCP penalizirana regresija
Područje	Psihometrija	Psihometrija
Obitelj	Latent structure	Latent structure
Godina nastanka	2001	2010
Tvorac	Jianqing Fan, Runze Li	Cun-Hui Zhang
Vrsta	Penalized regression with non-concave penalty	Penalized regression with minimax concave penalty
Temeljni izvor	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.	Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942.
Drugi nazivi	SCAD	MCP
Srodne	5	4
Sažetak	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.	MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD.