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A bivariate index vector for measuring departure from double symmetry in square contingency tables

Author

Listed:
  • Shuji Ando

    (Tokyo University of Science)

  • Kouji Tahata

    (Tokyo University of Science)

  • Sadao Tomizawa

    (Tokyo University of Science)

Abstract

For square contingency tables, a double symmetry model having a matrix structure that combines both symmetry and point symmetry was proposed. Also, an index which represents the degree of departure from double symmetry was proposed. However, this index cannot simultaneously characterize the degree of departure from symmetry and the degree of departure from point symmetry. For measuring the degree of departure from double symmetry, the present paper proposes a bivariate index vector that can simultaneously characterize the degree of departure from symmetry and the degree of departure from point symmetry.

Suggested Citation

  • Shuji Ando & Kouji Tahata & Sadao Tomizawa, 2019. "A bivariate index vector for measuring departure from double symmetry in square contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 519-529, June.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:2:d:10.1007_s11634-018-0320-7
    DOI: 10.1007/s11634-018-0320-7
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    Cited by:

    1. Shuji Ando, 2019. "A Bivariate Index for Visually Measuring Marginal Inhomogeneity in Square Tables," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(5), pages 58-65, September.

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