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Comparison of Shape Variability in Principal Component Biplot with Missing Values
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 Title & Authors
Comparison of Shape Variability in Principal Component Biplot with Missing Values
Shin, Sang-Min; Choi, Yong-Seok; Lee, Nae-Young;
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Biplots are the multivariate analogue of scatter plots. They are useful for giving a graphical description of the data matrix, for detecting patterns and for displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data matrix, most biplots are not directly applicable. In particular, we are interested in the shape variability of principal component biplot which is the most popular in biplots with missing values. For this, we estimate the missing data using the EM algorithm and mean imputation according to missing rates. Even though we estimate missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we propose a RMS(root mean square) for measuring and comparing the shape variability between the original biplots and the estimated biplots.
Biplots;EM algorithm;mean imputation;principal component biplot;RMS;shape variability;
 Cited by
결측값이 있는 정준상관 행렬도의 형상변동 연구,홍현욱;최용석;신상민;강창완;

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