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المجالالإحصاءالإحصاء
العائلةLatent structureLatent structure
سنة النشأة2000s (robust extensions of CA developed since the early 2000s)1984
صاحب الطريقةGreenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleaguesJean-Paul Benzécri; Michael Greenacre
النوعRobust dimension reduction for contingency tablesExploratory multivariate technique for categorical data
المصدر التأسيسيCroux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226. DOI ↗Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
الأسماء البديلةRCA, outlier-resistant correspondence analysis, robust CACA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
ذات صلة52
الملخصRobust 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.
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ScholarGateقارن الطرق: Robust Correspondence Analysis · Correspondence Analysis. استُرجع بتاريخ 2026-06-17 من https://scholargate.app/ar/compare