  In this work we present a rigorous application of the Expectation
Maximization algorithm to determine the marginal distributions and the
dependence structure in a Gaussian copula model with missing data. We further
show how to circumvent a priori assumptions on the marginals with
semiparametric modelling. The joint distribution learned through this algorithm
is considerably closer to the underlying distribution than existing methods.
