Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions
AbstractIn molecular sciences, the estimation of entropies of molecules is important for the understanding of many chemical and biological processes. Motivated by these applications, we consider the problem of estimating the entropies of circular random vectors and introduce non-parametric estimators based on circular distances between n sample points and their k th nearest neighbors (NN), where k (≤ n – 1) is a fixed positive integer. The proposed NN estimators are based on two different circular distances, and are proven to be asymptotically unbiased and consistent. The performance of one of the circular-distance estimators is investigated and compared with that of the already established Euclidean-distance NN estimator using Monte Carlo samples from an analytic distribution of six circular variables of an exactly known entropy and a large sample of seven internal-rotation angles in the molecule of tartaric acid, obtained by a realistic molecular-dynamics simulation. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Misra, N.; Singh, H.; Hnizdo, V. Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions. Entropy 2010, 12, 1125-1144.
Misra N, Singh H, Hnizdo V. Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions. Entropy. 2010; 12(5):1125-1144.Chicago/Turabian Style
Misra, Neeraj; Singh, Harshinder; Hnizdo, Vladimir. 2010. "Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions." Entropy 12, no. 5: 1125-1144.