Linganisha mbinu

	Usawazishaji wa Regressi kwa Adhabu ya SCAD	Urejeshaji Uliopigwa Faini wa MCP
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	2001	2010
Mwanzilishi	Jianqing Fan, Runze Li	Cun-Hui Zhang
Aina	Penalized regression with non-concave penalty	Penalized regression with minimax concave penalty
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.	Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942.
Majina mbadala	SCAD	MCP
Zinazohusiana	5	4
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.	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.