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