A copula transformation in multivariate mixed discrete-continuous models

Copulas allow a flexible and simultaneous modeling of complicated dependence structures together with various marginal distributions. Especially if the density function can be represented as product functions copula function, this leads to both an intuitive interpretation conditional distribution convenient estimation procedures. However, is no longer case for models mixed discrete continuous distributions, because corresponding cannot decomposed so nicely. In paper, we introduce transformation method that allows represent marginals probability mass/density function. With proposed method, distributions described analytically computational complexity in procedure reduced depending on type used.