The covariance between the functions of two random variables is obtained in terms of the cumulative distribution function. This result generalizes previous formulae given by W. Hoeffding (1940, Schriften Math. Inst. Univ. Berlin5, 181-233) and K. V. Mardia (1967, Biometrika54, 235-249). An...

Given a population and a random vector X, by using distances between observations of X, we prove that it is, in general, possible to construct probability densities for X. This distance-based approach can present problems, from a multidimensional scaling point of view, for some monotonic density...

As a generalization of the classical metric scaling solution for a finite set of points, a countable set of uncorrelated random variables is obtained from an arbitary continuous random variable X. The properties of these variables allow us to regard them as principal axes for X with respect to...

This paper provides a method of constructing multivariate distributions where both univariate marginals and a correlation matrix are given. An extension to multivariate marginals and a given intercorrelation matrix is also obtained. This method yields a family of distributions which are totally...