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SCAD-penalisoitu regressio×Eksploratiivinen rakennemallinnus×
TieteenalaPsykometriikkaPsykometriikka
MenetelmäperheLatent structureLatent structure
Syntyvuosi20012009
KehittäjäJianqing Fan, Runze LiTihomir Asparouhov, Bengt Muthén
TyyppiPenalized regression with non-concave penaltyHybrid exploratory-confirmatory factor modeling
AlkuperäislähdeFan, 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 ↗Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗
RinnakkaisnimetSCADESEM
Liittyvät55
TiivistelmäSCAD (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.Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.
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ScholarGateVertaile menetelmiä: SCAD Penalized Regression · Exploratory Structural Equation Modeling. Haettu 2026-06-18 osoitteesta https://scholargate.app/fi/compare