Asymptotics and optimal bandwidth selection for highest density region estimation

Samworth, RJ; Wand, MP

Asymptotics and optimal bandwidth selection for highest density region estimation

Samworth, RJ Wand, MP

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Publication Type:: Journal Article
Citation:: Annals of Statistics, 2010, 38 (3), pp. 1767 - 1792
Issue Date:: 2010-06-01

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Field	Value	Language
dc.contributor.author	Samworth, RJ	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2010-06-01	en_US
dc.identifier.citation	Annals of Statistics, 2010, 38 (3), pp. 1767 - 1792	en_US
dc.identifier.issn	0090-5364	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41302
dc.identifier.uri	http://hdl.handle.net/10453/13006
dc.description.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. © 2010 Institute of Mathematical Statistics.	en_US
dc.relation.ispartof	Annals of Statistics	en_US
dc.relation.isbasedon	10.1214/09-AOS766	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Asymptotics and optimal bandwidth selection for highest density region estimation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	38	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

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. © 2010 Institute of Mathematical Statistics.

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http://hdl.handle.net/10453/13006