방법 비교

	Robust Multiple Correspondence Analysis (Robust MCA)	대응 분석
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2000s	1984
창시자	Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)	Jean-Paul Benzécri; Michael Greenacre
유형	Robust multivariate dimension reduction	Exploratory multivariate technique for categorical data
원전	Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton. ISBN: 978-1498731775	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
별칭	Robust MCA, Outlier-resistant MCA, Robust HOMALS	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
관련	4	2
요약	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.	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.