Efficiency of nearest neighbor entropy estimators for Bernoulli measures

Evgeniy A. Timofeev; Alexei Kaltchenko

doi:10.1117/12.2049574

22 May 2014 Efficiency of nearest neighbor entropy estimators for Bernoulli measures

Evgeniy A. Timofeev, Alexei Kaltchenko

Proceedings Volume 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII; 911819 (2014) https://doi.org/10.1117/12.2049574
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Evgeniy A. Timofeev and 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); https://doi.org/10.1117/12.2049574

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