Dependence Structure of some Bivariate Distributions
Serdica Journal of Computing
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.
© 2014 Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Dimitrov, Boyan N., "Dependence Structure of some Bivariate Distributions" (2014). Mathematics Publications. 39.