Efficiency of nearest neighbor entropy estimators for Bernoulli measures

被引：0
|
作者：
Timofeev, Evgeniy A. [2 ]
Kaltchenko, Alexei [1 ]
机构：
[1] Wilfrid Laurier Univ, Dept Phys & Comp, Waterloo, ON N2L 3C5, Canada
[2] Yaroslavl State Univ, Dept Theoret Informat, Yaroslavl, Russia
来源：
INDEPENDENT COMPONENT ANALYSES, COMPRESSIVE SAMPLING, WAVELETS, NEURAL NET, BIOSYSTEMS, AND NANOENGINEERING XII | 2014年 / 9118卷
关键词：
Metric; entropy; estimation; nonparametric; nearest neighbor; bias;
D O I：
10.1117/12.2049574
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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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页数：5
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