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| Επεκτατική Ανάλυση Αντιστοιχιών (Robust Correspondence Analysis - RCA)× | Ανάλυση Αντιστοιχιών× | |
|---|---|---|
| Πεδίο | Στατιστική | Στατιστική |
| Οικογένεια | Latent structure | Latent structure |
| Έτος προέλευσης≠ | 2000s (robust extensions of CA developed since the early 2000s) | 1984 |
| Δημιουργός≠ | Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues | Jean-Paul Benzécri; Michael Greenacre |
| Τύπος≠ | Robust dimension reduction for contingency tables | Exploratory multivariate technique for categorical data |
| Θεμελιώδης πηγή≠ | 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. DOI ↗ | Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2 |
| Εναλλακτικές ονομασίες≠ | RCA, outlier-resistant correspondence analysis, robust CA | CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi |
| Συναφείς≠ | 5 | 2 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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