Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Regrese s penalizací MCP× | Parciální metoda nejmenších čtverců v modelování pomocí strukturálních rovnic× | |
|---|---|---|
| Obor | Psychometrika | Psychometrika |
| Rodina | Latent structure | Latent structure |
| Rok vzniku≠ | 2010 | 1985 |
| Tvůrce≠ | Cun-Hui Zhang | Herman Wold |
| Typ≠ | Penalized regression with minimax concave penalty | Component-based structural equation model |
| Původní zdroj≠ | Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942. DOI ↗ | Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445 |
| Další názvy≠ | MCP | PLS-SEM, PLS path modeling |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | 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. | PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data. |
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