Document Type
Article
Publication Date
2011
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Statistics
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.
Digital Commons Citation
Li, Shengqiao; Mnatsakanov, Robert M.; and Andrew, Michael E., "k-Nearest Neighbor Based Consistent Entropy Estimation for Hyperspherical Distributions" (2011). Faculty & Staff Scholarship. 2737.
https://researchrepository.wvu.edu/faculty_publications/2737
Source Citation
Li, S., Mnatsakanov, R. M., & Andrew, M. E. (2011). k-Nearest Neighbor Based Consistent Entropy Estimation for Hyperspherical Distributions. Entropy, 13(3), 650–667. https://doi.org/10.3390/e13030650
Comments
2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).