Robust Coplot Analysis

Abstract

CoPlot analysis is one of the multivariate data-visualizing techniques. It consists of two graphs: the first one represents the distribution of p-dimensional observations over two-dimensional space, whereas the second shows the relations of variables with the observations. At CoPlot analysis, multidimensional scaling (MDS) and Pearson’s correlation coefficient (PCC) are used to obtain a map that demonstrates observations and variables simultaneously. However, both MDS and PCC are sensitive to outliers. When multidimensional dataset contains outliers, interpretation of the map, which is obtained from classical CoPlot analysis, may result in wrong conclusions. At this study, a novel approach to classical CoPlot analysis is presented. By using robust MDS and median absolute deviation correlation coefficient (MADCC), robust CoPlot map is improved. Numerical examples are given to illustrate the merits of the proposed approach. Also, obtained results are compared with the classical CoPlot analysis to emphasize the superiority of introduced robust CoPlot approach.

Keywords:

• MAD correlation coefficient
• Median
• Multidimensional scaling
• Outlier

Mathematics Subject Classification:

• Primary 62-09; Secondary 91C15