পদ্ধতির তুলনা করুন

	দৃঢ় সঙ্গতি বিশ্লেষণ	কোরস্পন্ডেন্স অ্যানালাইসিস
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	2000s (robust extensions of CA developed since the early 2000s)	1984
প্রবর্তক	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Jean-Paul Benzécri; Michael Greenacre
ধরন	Robust dimension reduction for contingency tables	Exploratory multivariate technique for categorical data
মৌলিক উৎস	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
অপর নাম	RCA, outlier-resistant correspondence analysis, robust CA	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
সম্পর্কিত	5	2
সারসংক্ষেপ	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.