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Investigation on the high-order approximation of the entropy bias

Author

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  • Ramos, Antônio M.T.
  • Casagrande, Helder L.
  • Macau, Elbert E.N.

Abstract

The estimation of entropy from experimental data has a considerable bias when the discretization of the variable domain is comparable to the sample size. In this case, the source of the bias is the difference between the a priori distribution and the observed distribution from sampled data.

Suggested Citation

  • Ramos, Antônio M.T. & Casagrande, Helder L. & Macau, Elbert E.N., 2020. "Investigation on the high-order approximation of the entropy bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    • Handle: RePEc:eee:phsmap:v:549:y:2020:i:c:s037843712030090x
    DOI: 10.1016/j.physa.2020.124301
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    1. Jakob Runge & Vladimir Petoukhov & Jonathan F. Donges & Jaroslav Hlinka & Nikola Jajcay & Martin Vejmelka & David Hartman & Norbert Marwan & Milan Paluš & Jürgen Kurths, 2015. "Identifying causal gateways and mediators in complex spatio-temporal systems," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
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    3. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    4. Bedartha Goswami & Niklas Boers & Aljoscha Rheinwalt & Norbert Marwan & Jobst Heitzig & Sebastian F. M. Breitenbach & Jürgen Kurths, 2018. "Abrupt transitions in time series with uncertainties," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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