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SCAD-reglerad regression×Multifaktoriell analys×
ÄmnesområdePsykometriPsykometri
FamiljLatent structureLatent structure
Ursprungsår20011985
UpphovspersonJianqing Fan, Runze LiBrigitte Escofier, Jérôme Pagès
TypPenalized regression with non-concave penaltyMultiblock dimension reduction
UrsprungskällaFan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360. DOI ↗Escofier, B., & Pagès, J. (1985). Analyses factorielles simples et multiples : Objectifs, méthodes et interprétation. Dunod. ISBN: 9782040116835
AliasSCADMFA, MFA multiple
Närliggande55
SammanfattningSCAD (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.
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ScholarGateJämför metoder: SCAD Penalized Regression · Multiple Factor Analysis. Hämtad 2026-06-17 från https://scholargate.app/sv/compare