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<p>The coefficient of variation, which measures the dispersion of a distribution around its mean, is not uniquely defined in the multidimensional case, and so is the Gini index, which measures the dispersion of a distribution in terms of the mean differences among its observations. In this paper, we bridge these two notions of dispersion by proposing a multidimensional coefficient of variation which generalizes the Gini index. We show that our proposed multivariate coefficient of variation coincides with Voinov-Nikulin’s coefficient of variation up to a multiplicative factor. We then introduce and study a set of four properties that a multivariate coefficient of variation should have and show that our proposal possesses all of them. Our proposal is practically compared with the existing multivariate coefficients of variation on both real and simulated data.</p>

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How to Measure Multidimensional Variation?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Gennaro Auricchio,
  • Paolo Giudici,
  • Giuseppe Toscani

摘要

The coefficient of variation, which measures the dispersion of a distribution around its mean, is not uniquely defined in the multidimensional case, and so is the Gini index, which measures the dispersion of a distribution in terms of the mean differences among its observations. In this paper, we bridge these two notions of dispersion by proposing a multidimensional coefficient of variation which generalizes the Gini index. We show that our proposed multivariate coefficient of variation coincides with Voinov-Nikulin’s coefficient of variation up to a multiplicative factor. We then introduce and study a set of four properties that a multivariate coefficient of variation should have and show that our proposal possesses all of them. Our proposal is practically compared with the existing multivariate coefficients of variation on both real and simulated data.