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| 강건 요인 분석(Robust Factor Analysis)× | 강건 공분산 추정 (MCD)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2003 | 1999 |
| 창시자≠ | Pison, Rousseeuw, Filzmoser & Croux | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| 유형≠ | Robust latent-factor model | Robust multivariate location-scatter estimator |
| 원전≠ | 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 ↗ |
| 별칭 | 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) |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. |
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