Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Robust Factor Analysis× | Mõju diagnostika (Cooki kaugus, DFFITS, võimendus)× | Pricipaalanalüüs× | |
|---|---|---|---|
| Valdkond≠ | Statistika | Statistika | Masinõpe |
| Perekond≠ | Regression model | Regression model | Machine learning |
| Tekkeaasta≠ | 2003 | 1977 | 2002 |
| Looja≠ | Pison, Rousseeuw, Filzmoser & Croux | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tüüp≠ | Robust latent-factor model | Regression diagnostic | Unsupervised dimensionality reduction |
| Algallikas≠ | Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172. DOI ↗ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Rööpnimetused≠ | robust factor analysis, outlier-resistant factor analysis, MCD-based factor analysis, Robust Faktör Analizi | Cook's distance, DFFITS, leverage, influential observation detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Seotud≠ | 5 | 5 | 3 |
| Kokkuvõte≠ | 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. | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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