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	Robust PCA	因子分析	頑健回帰
分野	統計学	研究統計	統計学
系統	Regression model	Process / pipeline	Regression model
提唱年	2011	1931	1964
提唱者	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Louis Leon Thurstone	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
種類	Robust dimensionality reduction / matrix decomposition	Method	Regression with outlier resistance
原典	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
別名	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)	EFA, CFA, latent variable modeling	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
関連	3	3	6
概要	Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.