方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| MCP惩罚回归× | 探索性结构方程模型× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2010 | 2009 |
| 提出者≠ | Cun-Hui Zhang | Tihomir Asparouhov, Bengt Muthén |
| 类型≠ | Penalized regression with minimax concave penalty | Hybrid exploratory-confirmatory factor modeling |
| 开创性文献≠ | 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 ↗ |
| 别名 | MCP | ESEM |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|