  A novel method for correlation analysis using scale-dependent Renyi entropies
is described. The method involves calculating the entropy of a data
distribution as an explicit function of the scale of a d-dimensional partition
of d-cubes, which is dithered to remove bias. Analytic expressions for dithered
scale-local entropy and dimension for a uniform random point set are derived
and compared to Monte Carlo results. Simulated nontrivial point-set
correlations representing condensation and clustering are similarly analyzed.
