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

Shuji Ando (), Kouji Tahata () and Sadao Tomizawa ()
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Shuji Ando: Tokyo University of Science
Kouji Tahata: Tokyo University of Science
Sadao Tomizawa: Tokyo University of Science

Advances in Data Analysis and Classification, 2019, vol. 13, issue 2, No 9, 519-529

Abstract: 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.

Keywords: Confidence region; Double symmetry; Index vector; Visualization; 62H17 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-018-0320-7

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