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| Régression pénalisée SCAD× | Analyse Multi-Factorielle× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2001 | 1985 |
| Auteur d'origine≠ | Jianqing Fan, Runze Li | Brigitte Escofier, Jérôme Pagès |
| Type≠ | Penalized regression with non-concave penalty | Multiblock dimension reduction |
| Source fondatrice≠ | 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. DOI ↗ | Escofier, B., & Pagès, J. (1985). Analyses factorielles simples et multiples : Objectifs, méthodes et interprétation. Dunod. ISBN: 9782040116835 |
| Alias≠ | SCAD | MFA, MFA multiple |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. |
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