\(K\)-nearest neighbor based consistent entropy estimation for hyperspherical distributions
type: Item
instance of: scholarly article
title: \(K\)-nearest neighbor based consistent entropy estimation for hyperspherical distributions
zbMATH Open document ID: 1229.62003
author: Shengqiao Li
author: Robert Mnatsakanov
author: Michael E. Andrew
published in: Entropy
publication date: 2012-01-09T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62B10
Mathematics Subject Classification ID: 62H11
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 5995940
zbMATH Keywords: hyperspherical distribution
zbMATH Keywords: directional data
zbMATH Keywords: differential entropy
zbMATH Keywords: cross entropy
zbMATH Keywords: Kullback-Leibler divergence
zbMATH Keywords: k-nearest neighbor
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e13030650
OpenAlex ID: W2051750307
Wikidata QID: Q23921331
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DOI: 10.3390/E13030650
description: scientific article; zbMATH DE number 5995940