  We introduce some new indexes to measure the departure of any multivariate
continuous distribution on non-negative orthant from a given reference one such
the uncorrelated exponential model, similar to the relative Fisher dispersion
indexes of multivariate count models.
  The proposed multivariate variation indexes are scalar quantities, defined as
ratios of two quadratic forms of the mean vector and the covariance matrix.
They can be used to discriminate between continuous positive distributions.
Generalized and multiple marginal variation indexes with and without
correlation structure, respectively, and their relative extensions are
discussed. The asymptotic behavior and other properties are studied.
Illustrative examples and numerical applications are analyzed under several
scenarios, leading to appropriate choices of multivariate models. Some
concluding remarks and possible extensions are made.
