Undersmoothed Kernel Entropy Estimators
Paninski, L.
Yajima, M.
Dept. of Stat., Columbia Univ., New York, NY;
This paper appears in: Information Theory, IEEE Transactions on
Publication Date: Sept. 2008
Volume: 54,
Issue: 9
On page(s): 4384-4388
ISSN: 0018-9448
INSPEC Accession Number: 10180437
Digital Object Identifier: 10.1109/TIT.2008.928251
Current Version Published: 2008-08-26
Abstract
We develop a ldquoplug-inrdquo 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 independent and identically distributed (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.
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