Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Регресия с наказание MCP× | Изследователско моделиране на структурни уравнения× | |
|---|---|---|
| Област | Психометрия | Психометрия |
| Семейство | Latent structure | Latent structure |
| Година на възникване≠ | 2010 | 2009 |
| Създател≠ | Cun-Hui Zhang | Tihomir Asparouhov, Bengt Muthén |
| Тип≠ | Penalized regression with minimax concave penalty | Hybrid exploratory-confirmatory factor modeling |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | MCP | ESEM |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|