  Numerical estimates of the Kolmogorov-Sinai entropy based on a finite amount
of data decay towards zero in the relevant limits. Rewriting differences of
block entropies as averages over decay rates, and ignoring all parts of the
sample where these rates are uncomputable because of the lack of neighbours,
yields improved entropy estimates. In the same way, the scaling range for
estimates of the information dimension can be extended considerably. The
improvement is demonstrated for experimental data.
