Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression pénalisée MCP× | Modélisation Exploratoire par Équations Structurelles× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2010 | 2009 |
| Auteur d'origine≠ | Cun-Hui Zhang | Tihomir Asparouhov, Bengt Muthén |
| Type≠ | Penalized regression with minimax concave penalty | Hybrid exploratory-confirmatory factor modeling |
| Source fondatrice≠ | Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942. DOI ↗ | Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗ |
| Alias | MCP | ESEM |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | 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. |
| ScholarGateJeu de données ↗ |
|
|