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Entropy 2013, 15(6), 1999-2011; doi:10.3390/e15061999

Bias Adjustment for a Nonparametric Entropy Estimator

Department of Mathematics and Statistics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
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Received: 20 March 2013 / Revised: 8 May 2013 / Accepted: 17 May 2013 / Published: 23 May 2013
(This article belongs to the Special Issue Estimating Information-Theoretic Quantities from Data)
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Abstract

Zhang in 2012 introduced a nonparametric estimator of Shannon’s entropy, whose bias decays exponentially fast when the alphabet is finite. We propose a methodology to estimate the bias of this estimator. We then use it to construct a new estimator of entropy. Simulation results suggest that this bias adjusted estimator has a significantly lower bias than many other commonly used estimators. We consider both the case when the alphabet is finite and when it is countably infinite.
Keywords: nonparametric entropy estimation; bias nonparametric entropy estimation; bias
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zhang, Z.; Grabchak, M. Bias Adjustment for a Nonparametric Entropy Estimator. Entropy 2013, 15, 1999-2011.

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