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Proceedings Paper

Efficiency of nearest neighbor entropy estimators for Bernoulli measures
Author(s): Evgeniy A. Timofeev; Alexei Kaltchenko
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Paper Abstract

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.

Paper Details

Date Published: 22 May 2014
PDF: 5 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 911819 (22 May 2014); doi: 10.1117/12.2049574
Show Author Affiliations
Evgeniy A. Timofeev, Yaroslavl State Univ. (Russian Federation)
Alexei Kaltchenko, Wilfrid Laurier Univ. (Canada)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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