응용통계연구 (The Korean Journal of Applied Statistics)

  • 제21권6호
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  • Pages.1109-1116
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  • 2008
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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Comparison of Shape Variability in Principal Component Biplot with Missing Values

  • 발행 : 2008.12.31
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초록

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.

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참고문헌

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  10. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data, Chapman & Hall/CRC, London
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피인용 문헌

  1. A Study on Shape Variability in Canonical Correlation Biplot with Missing Values vol.23, pp.5, 2010, https://doi.org/10.5351/KJAS.2010.23.5.955
  2. A robust AMMI model for the analysis of genotype-by-environment data 2015, https://doi.org/10.1093/bioinformatics/btv533