Dependence Structure of some Bivariate Distributions

Abstract

Dependence in the world of uncertainty is a complex concept.
However, it exists, is asymmetric, has magnitude and direction, and can be
measured. We use some measures of dependence between random events to
illustrate how to apply it in the study of dependence between non-numeric
bivariate variables and numeric random variables. Graphics show what is
the inner dependence structure in the Clayton Archimedean copula and the
Bivariate Poisson distribution. We know this approach is valid for studying
the local dependence structure for any pair of random variables determined
by its empirical or theoretical distribution. And it can be used also to simulate
dependent events and dependent r/v/’s, but some restrictions apply.

ACM Computing Classification System (1998): G.3, J.2.