Coefficients of Structural Association

Stan Lipovetsky and Igor Mandel ()
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Stan Lipovetsky: GfK Custom Research North America, 8401 Golden Valley Road, Minneapolis, MN 55427, USA
Igor Mandel: Telmar Group Inc., 470 Park Ave South, New York, NY 10018, USA

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 02, 285-313

Abstract: We consider estimation of one variable’s dependence against another one in a new measure called a coefficient of structural association (CSA). It is based on the distribution of one variable along the segments of another one, and yields a gauge similar to the correlation ratio in the nonlinear regression modeling. This index can be constructed as a quotient of the observed and maximum possible variances. The CSA relations to other measures of dependence are described too, particularly, for binary variables CSA reduces to the Loevinger’s coefficient of association. Numerical simulations show that CSA presents a powerful tool for data analysis where traditional measures fail. This method can enrich both theoretical and practical estimations for identifying hidden patterns in the data and help managers and researchers in taking appropriate decisions.

Keywords: Coefficient of association; percentile; maximum variance; equidistant variance (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:16:y:2017:i:02:n:s0219622014500552

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DOI: 10.1142/S0219622014500552

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