  A Copula density estimation method that is based on a finite mixture of
heterogeneous parametric copula densities is proposed here. More specifically,
the mixture components are Clayton, Frank, Gumbel, T, and normal copula
densities, which are capable of capturing lower tail,strong central, upper
tail, heavy tail, and symmetrical elliptical dependence, respectively. The
model parameters are estimated by an interior-point algorithm for the
constrained maximum likelihood problem. The interior-point algorithm is
compared with the commonly used EM algorithm. Simulation and real data
application show that the proposed approach is effective to model complex
dependencies for data in dimensions beyond two or three.
