22 May 2014 Efficiency of nearest neighbor entropy estimators for Bernoulli measures
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A problem of nonparametric entropy estimation for discrete stationary ergodic processes is considered. The estimation is based on so-called ”nearest-neighbor method”. It is shown that, for Bernoulli measures, the estimator is unbiased, i.e. converges to the (inverse) entropy of the process. Moreover, for symmetric Bernoulli measures, the unbiased estimator can be explicitly constructed.
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Evgeniy A. Timofeev, Evgeniy A. Timofeev, Alexei Kaltchenko, Alexei Kaltchenko, "Efficiency of nearest neighbor entropy estimators for Bernoulli measures", Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 911819 (22 May 2014); doi: 10.1117/12.2049574; https://doi.org/10.1117/12.2049574

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