পদ্ধতির তুলনা করুন

	MCP পেনাল্টিযুক্ত রিগ্রেশন	SCAD দণ্ডায়িত রিগ্রেশন
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	2010	2001
প্রবর্তক	Cun-Hui Zhang	Jianqing Fan, Runze Li
ধরন	Penalized regression with minimax concave penalty	Penalized regression with non-concave penalty
মৌলিক উৎস	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.
অপর নাম	MCP	SCAD
সম্পর্কিত	4	5
সারসংক্ষেপ	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.