A suggestion for a multivariate concordance coefficient
In the present paper we will introduce a coecient of multivariate association i.e. association in a d-variate vector of observations x = (x1; : : : ; xd), where d 2 and where each xj is itself a vector of n observations. We order the observations, divide them in slices and count how many times one observation in the r-th slice of any of the d distributions also belongs to the r-th slice of any of the others. The greater the number of overlaps between the units belonging to corresponding slices, the greater the concordance between the d distributions. This is the simple and intuitive idea our multivariate association coecient stems from. It is in fact a multidimensional concordance coecient since it assumes comonotonicity for all variables.
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- Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
- Koen DECANCQ, 2010.
"Copula-based orderings of multivariate dependence,"
Working Papers Department of Economics
ces10.08, KU Leuven, Faculty of Economics and Business, Department of Economics.
- DECANCQ, Koen, 2010. "Copula-based orderings of multivariate dependence," CORE Discussion Papers 2010012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
- M. Taylor, 2007. "Multivariate measures of concordance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(4), pages 789-806, December. Full references (including those not matched with items on IDEAS)
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