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Abstract
Copulas have become an important analytic tool for characterizing multivariate
distributions and dependence. One is often interested in simulating data from copula
estimates. The process can be analytically and computationally complex and usually
involves steps that are unique to a given parametric copula. We describe an alternative
approach that uses \probability{proportional{to{size" (PPS) random sampling with
weights formed from the copula likelihood function. The method is
flexible and can be
applied to parametric and nonparametric marginal density estimates. The precision of
the simulation can be calibrated by adjusting the density of the multidimensional grid
used in the simulation process. The approach is fully transparent to any copula function
with continuous random variables. An example evaluates a number of goodness-of-fit
criteria and provides strong support for the validity and practicality of the method.