  Entropy and its various generalizations are important in many fields,
including mathematical statistics, communication theory, physics and computer
science, for characterizing the amount of information associated with a
probability distribution. In this paper we propose goodness-of-fit statistics
for the multivariate Student and multivariate Pearson type II distributions,
based on the maximum entropy principle and a class of estimators for R\'{e}nyi
entropy based on nearest neighbour distances. We prove the L^2-consistency of
these statistics using results on the subadditivity of Euclidean functionals on
nearest neighbour graphs, and investigate their rate of convergence and
asymptotic distribution using Monte Carlo methods. In addition we present a
novel iterative method for estimating the shape parameter of the multivariate
Student and multivariate Pearson type II distributions.
