  Vine copulas are a flexible tool for multivariate non-Gaussian distributions.
For data from an observational study where the explanatory variables and
response variables are measured together, a proposed vine copula regression
method uses regular vines and handles mixed continuous and discrete variables.
This method can efficiently compute the conditional distribution of the
response variable given the explanatory variables. The performance of the
proposed method is evaluated on simulated data sets and a real data set. The
experiments demonstrate that the vine copula regression method is superior to
linear regression in making inferences with conditional heteroscedasticity.
