Undersmoothed kernel entropy estimators
In press, IEEE Transactions on Information Theory
We develop a ``plug-in'' kernel estimator for the differential entropy
that is consistent even if the kernel width tends to zero as quickly
as $1/N$, where $N$ is the number of i.i.d. samples. Thus, accurate
density estimates are not required for accurate kernel entropy
estimates; in fact, it is a good idea when estimating entropy to
sacrifice some accuracy in the quality of the corresponding density
estimate.
Reprint | Liam
Paninski's research page