Robust Coplot Analysis


Atilgan Y.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.45, sa.5, ss.1763-1775, 2016 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45 Konu: 5
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1080/03610918.2013.875571
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Sayfa Sayıları: ss.1763-1775

Özet

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.

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.