  In this paper, we study the strong consistency of a bias reduced kernel
density estimator and derive a strongly con- sistent Kullback-Leibler
divergence (KLD) estimator. As application, we formulate a goodness-of-fit test
and an asymptotically standard normal test for model selection. The Monte Carlo
simulation show the effectiveness of the proposed estimation methods and
statistical tests.
