Affiliation:
1. Department of Information and Computer Science, Helsinki University of TechnologyPO Box 5400, 02015 TKK Espoo, Finland
Abstract
In this paper, bounds on the mean power-weighted nearest neighbour distance are derived. Previous work concentrates mainly on the infinite sample limit, whereas our bounds hold for any sample size. The results are expected to be of importance, for example in statistical physics, non-parametric statistics and computational geometry, where they are related to the structure of matter as well as properties of statistical estimators and random graphs.
Subject
General Physics and Astronomy,General Engineering,General Mathematics
Cited by
25 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Reweighting samples under covariate shift using a Wasserstein distance criterion;Electronic Journal of Statistics;2022-01-01
2. The 1-nearest neighbor regression function estimate;Lectures on the Nearest Neighbor Method;2015
3. Entropy estimation;Lectures on the Nearest Neighbor Method;2015
4. The nearest neighbor distance;Lectures on the Nearest Neighbor Method;2015
5. The nearest neighbor rule: variable k;Lectures on the Nearest Neighbor Method;2015