Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustā faktoru analīze× | Robustu kovariācijas novērtēšana (MCD)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2003 | 1999 |
| Autors≠ | Pison, Rousseeuw, Filzmoser & Croux | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| Tips≠ | Robust latent-factor model | Robust multivariate location-scatter estimator |
| Pirmavots≠ | Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172. DOI ↗ | Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223. DOI ↗ |
| Citi nosaukumi | robust factor analysis, outlier-resistant factor analysis, MCD-based factor analysis, Robust Faktör Analizi | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Robust Factor Analysis recovers the latent factor structure of multivariate continuous data while resisting the distorting pull of outliers. Introduced by Pison, Rousseeuw, Filzmoser and Croux (2003), it replaces the classical sample covariance with a robust estimator such as the Minimum Covariance Determinant (MCD) or an S-estimator before extracting factors. | Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation. |
| ScholarGateDatu kopa ↗ |
|
|