Abstract
The sum of the entropies of the components of a random vector minus the entropy of the whole vector is known to be a good measure of global interdependence. This measure is used for selecting a relevant pair (or triple) of coordinates based on both the low degree of interdependence exhibited by them and their high degree of dependence on the omitted variables. When the characteristics are Gaussian, this global measure of interdependence has a simple formula, easily calculated from the information offered by MINITAB or SAS. The formalism is applied both to an example involving discrete coordinates and to the known “rabbit's head” diabetes example where the coordinates have continuous range.
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