방법 비교

	강건 대응 분석	대응 분석
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2000s (robust extensions of CA developed since the early 2000s)	1984
창시자	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Jean-Paul Benzécri; Michael Greenacre
유형	Robust dimension reduction for contingency tables	Exploratory multivariate technique for categorical data
원전	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. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
별칭	RCA, outlier-resistant correspondence analysis, robust CA	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
관련	5	2
요약	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.	Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.