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k-Nearest neighbor based consistent entropy estimation for hyperspherical distributions.

Authors
Li-S; Mnatsakanov-RM; Andrew-ME
Source
Entropy 2011 Mar; 13(3):650-667
NIOSHTIC No.
20038421
Abstract
A consistent entropy estimator for hyperspherical data is proposed based on the k-nearest neighbor (knn) approach. The asymptotic unbiasedness and consistency of the estimator are proved. Moreover, cross entropy and Kullback-Leibler (KL) divergence estimators are also discussed. Simulation studies are conducted to assess the performance of the estimators for models including uniform and von Mises-Fisher distributions. The proposed knn entropy estimator is compared with the moment based counterpart via simulations. The results show that these two methods are comparable.
Keywords
Mathematical-models; Molecular-biology; Quantitative-analysis; Standards; Statistical-analysis; Author Keywords: hyperspherical distribution; directional data; differential entropy; cross entropy; Kullback-Leibler divergence; k-nearest neighbor
Contact
Shengqiao Li, Health Effects Laboratory Division, National Institute for ccupational Safety and Health, Morgantown, WV 26505
CODEN
ENTRFG
Publication Date
20110308
Document Type
Journal Article
Email Address
Shengqiao.Li@cdc.hhs.gov
Fiscal Year
2011
NTIS Accession No.
NTIS Price
Issue of Publication
3
ISSN
1099-4300
NIOSH Division
HELD
Priority Area
Services: Public Safety
Source Name
Entropy
State
WV
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