Entropy estimates of small data sets

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dc.creatorBonachela, Juan A.
dc.creatorHinrichsen, Haye
dc.creatorMunoz, Miguel A.
dc.date2008-04-29
dc.date.accessioned2026-07-07T09:35:49Z
dc.date.available2026-07-07T09:35:49Z
dc.descriptionEstimating 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.
dc.description11 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/0804.4561
dc.identifierhttp://arxiv.org/abs/0804.4561
dc.identifierJ. Phys. A: Math. Theor. 41 (2008) 202001
dc.identifierdoi:10.1088/1751-8113/41/20/202001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/159961
dc.subjectStatistical Mechanics
dc.subjectQuantitative Methods
dc.titleEntropy estimates of small data sets
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