Density Level Sets: Asymptotics, Inference, and Visualization
Methodology
2016-09-06 v2 Statistics Theory
Statistics Theory
Abstract
We derive asymptotic theory for the plug-in estimate for density level sets under Hausdoff loss. Based on the asymptotic theory, we propose two bootstrap confidence regions for level sets. The confidence regions can be used to perform tests for anomaly detection and clustering. We also introduce a technique to visualize high dimensional density level sets by combining mode clustering and multidimensional scaling.
Cite
@article{arxiv.1504.05438,
title = {Density Level Sets: Asymptotics, Inference, and Visualization},
author = {Yen-Chi Chen and Christopher R. Genovese and Larry Wasserman},
journal= {arXiv preprint arXiv:1504.05438},
year = {2016}
}
Comments
Accepted to JASA-T&M. 40 pages, 11 figures