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arXiv · 0804.4561

Entropy estimates of small data sets

Juan A. Bonachela·Haye Hinrichsen·Miguel A. Munoz
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Abstract

Estimating entropies from limited data series is known to be a non-trivial task. Naive estimations are plagued with both systematic (bias) and statistical errors. Here, we present a new 'balanced estimator' for entropy functionals Shannon, Rényi and Tsallis) specially devised to provide a compromise between low bias and small statistical errors, for short data series. This new estimator out-performs other currently available ones when the data sets are small and the probabilities of the possible outputs of the random variable are not close to zero. Otherwise, other well-known estimators remain a better choice. The potential range of applicability of this estimator is quite broad specially for biological and digital data series.

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BibTeXRIS

Juan A. Bonachela, Haye Hinrichsen, Miguel A. Munoz. 2008-04-29. Entropy estimates of small data sets. https://doi.org/10.1088/1751-8113/41/20/202001

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