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	Odporna analiza korespondencji	Robustowa Wielowymiarowa Analiza Korespondencji (Robust MCA)
Dziedzina	Statystyka	Statystyka
Rodzina	Latent structure	Latent structure
Rok powstania	2000s (robust extensions of CA developed since the early 2000s)	2000s
Twórca	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)
Typ	Robust dimension reduction for contingency tables	Robust multivariate dimension reduction
Źródło pierwotne	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.	Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton. ISBN: 978-1498731775
Inne nazwy	RCA, outlier-resistant correspondence analysis, robust CA	Robust MCA, Outlier-resistant MCA, Robust HOMALS
Pokrewne	5	4
Podsumowanie	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 Multiple Correspondence Analysis extends classical MCA to datasets containing outlying or atypical rows of categorical data. By downweighting influential observations before the singular value decomposition, it produces a low-dimensional map of category relationships that faithfully represents the bulk of the data rather than being distorted by a handful of anomalous cases.