Asymptotics and optimal bandwidth selection for highest density region estimation
Statistics Theory
2010-10-05 v1 Statistics Theory
Abstract
We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is appropriate for HDR estimation. This approximation is then used to derive a bandwidth selection rule for HDR estimation possessing attractive asymptotic properties. We also present the results of numerical studies that illustrate the benefits of our theory and methodology.
Cite
@article{arxiv.1010.0591,
title = {Asymptotics and optimal bandwidth selection for highest density region estimation},
author = {R. J. Samworth and M. P. Wand},
journal= {arXiv preprint arXiv:1010.0591},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/09-AOS766 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)