Сравнение методов

	Робастный анализ соответствий	Робастный эксплораторный факторный анализ
Область	Статистика	Психометрия
Семейство	Latent structure	Latent structure
Год появления	2000s (robust extensions of CA developed since the early 2000s)	2000–2003
Автор метода	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Тип	Robust dimension reduction for contingency tables	Latent variable / dimension reduction (robust)
Основополагающий источник	Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
Другие названия	RCA, outlier-resistant correspondence analysis, robust CA	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Связанные	5	4
Сводка	Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.