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。
| 稳健因子分析× | 影响诊断 (库克距离, DFFITS, 杠杆率)× | 主成分分析× | |
|---|---|---|---|
| 领域≠ | 统计学 | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Regression model | Machine learning |
| 起源年份≠ | 2003 | 1977 | 2002 |
| 提出者≠ | Pison, Rousseeuw, Filzmoser & Croux | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| 类型≠ | Robust latent-factor model | Regression diagnostic | Unsupervised dimensionality reduction |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | 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 |
| 相关≠ | 5 | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|
|