विधियों की तुलना करें

	MCP दंडित प्रतिगमन	SCAD दंडित प्रतिगमन (SCAD Penalized Regression)
क्षेत्र	मनोमिति	मनोमिति
परिवार	Latent structure	Latent structure
उद्भव वर्ष	2010	2001
प्रवर्तक	Cun-Hui Zhang	Jianqing Fan, Runze Li
प्रकार	Penalized regression with minimax concave penalty	Penalized regression with non-concave penalty
मौलिक स्रोत	Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942.	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.
उपनाम	MCP	SCAD
संबंधित	4	5
सारांश	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.	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.