  Recent work has focused on the problem of nonparametric estimation of
information divergence functionals. Many existing approaches are restrictive in
their assumptions on the density support set or require difficult calculations
at the support boundary which must be known a priori. The MSE convergence rate
of a leave-one-out kernel density plug-in divergence functional estimator for
general bounded density support sets is derived where knowledge of the support
boundary is not required. The theory of optimally weighted ensemble estimation
is generalized to derive a divergence estimator that achieves the parametric
rate when the densities are sufficiently smooth. The asymptotic distribution of
this estimator and some guidelines for tuning parameter selection are provided.
Based on the theory, an empirical estimator of R\'{e}nyi-$\alpha$ divergence is
proposed that outperforms the standard kernel density plug-in estimator,
especially in high dimension. The estimator is shown to be robust to the choice
of tuning parameters. As an illustration, we use the estimator to estimate
bounds on the Bayes error rate of a classification problem.
