  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.
