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A Study on Shape Variability in Canonical Correlation Biplot with Missing Values
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 Title & Authors
A Study on Shape Variability in Canonical Correlation Biplot with Missing Values
Hong, Hyun-Uk; Choi, Yong-Seok; Shin, Sang-Min; Ka, Chang-Wan;
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Canonical correlation biplot is a useful biplot for giving a graphical description of the data matrix which consists of the association between two sets of variables, for detecting patterns and displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data, most biplots are not directly applicable. To solve this problem, we estimate the missing data using the median, mean, EM algorithm and MCMC imputation methods according to missing rates. Even though we estimate the missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we use a RMS(root mean square) which was proposed by Shin et al. (2007) and PS(procrustes statistic) for measuring and comparing the shape variability between the original biplots and the estimated biplots.
Canonical correlation biplot, shape variability;procrustes;missing mechanism;imputation methods;
 Cited by
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