השוואת שיטות

	רגרסיה עם עונש SCAD	ניתוח גורמים מרובה
תחום	פסיכומטריה	פסיכומטריה
משפחה	Latent structure	Latent structure
שנת המקור	2001	1985
הוגה השיטה	Jianqing Fan, Runze Li	Brigitte Escofier, Jérôme Pagès
סוג	Penalized regression with non-concave penalty	Multiblock dimension reduction
מקור מכונן	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.	Escofier, B., & Pagès, J. (1985). Analyses factorielles simples et multiples : Objectifs, méthodes et interprétation. Dunod. ISBN: 9782040116835
כינויים	SCAD	MFA, MFA multiple
קשורות	5	5
תקציר	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.	Multiple Factor Analysis (MFA) is a dimension reduction technique developed by Escofier and Pagès (1985) for analyzing multiple groups of variables measured on the same observations. MFA balances the influence of each variable group to provide a unified view of how observations relate across multiple perspectives.