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Article: Hybrid confidence regions based on data depth

TitleHybrid confidence regions based on data depth
Authors
KeywordsData Depth
Hybrid Confidence Region
Pivot
Robustness
Issue Date2012
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal Of The Royal Statistical Society. Series B: Statistical Methodology, 2012, v. 74 n. 1, p. 91-109 How to Cite?
AbstractWe consider the general problem of constructing confidence regions for, possibly multi-dimensional, parameters when we have available more than one approach for the construction. These approaches may be motivated by different model assumptions, different levels of approximation, different settings of tuning parameters or different Monte Carlo algorithms. Their effectiveness is often governed by different sets of conditions which are difficult to vindicate in practice. We propose two procedures for constructing hybrid confidence regions which endeavour to integrate all such individual approaches. The procedures employ the concept of data depth to calibrate the confidence region in two different ways, the first rendering its coverage error minimax and the second rendering its coverage error conservative. The resulting region reconciles in many important aspects the discrepancies between the various approaches, and is robust against misspecification of their governing conditions. Theoretical and empirical properties of our procedures are investigated in comparison with those of the constituent individual approaches. © 2011 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/172490
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLee, SMSen_US
dc.date.accessioned2012-10-30T06:22:46Z-
dc.date.available2012-10-30T06:22:46Z-
dc.date.issued2012en_US
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 2012, v. 74 n. 1, p. 91-109en_US
dc.identifier.issn1369-7412en_US
dc.identifier.urihttp://hdl.handle.net/10722/172490-
dc.description.abstractWe consider the general problem of constructing confidence regions for, possibly multi-dimensional, parameters when we have available more than one approach for the construction. These approaches may be motivated by different model assumptions, different levels of approximation, different settings of tuning parameters or different Monte Carlo algorithms. Their effectiveness is often governed by different sets of conditions which are difficult to vindicate in practice. We propose two procedures for constructing hybrid confidence regions which endeavour to integrate all such individual approaches. The procedures employ the concept of data depth to calibrate the confidence region in two different ways, the first rendering its coverage error minimax and the second rendering its coverage error conservative. The resulting region reconciles in many important aspects the discrepancies between the various approaches, and is robust against misspecification of their governing conditions. Theoretical and empirical properties of our procedures are investigated in comparison with those of the constituent individual approaches. © 2011 Royal Statistical Society.en_US
dc.languageengen_US
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_US
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_US
dc.subjectData Depthen_US
dc.subjectHybrid Confidence Regionen_US
dc.subjectPivoten_US
dc.subjectRobustnessen_US
dc.titleHybrid confidence regions based on data depthen_US
dc.typeArticleen_US
dc.identifier.emailLee, SMS: smslee@hku.hken_US
dc.identifier.authorityLee, SMS=rp00726en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1111/j.1467-9868.2011.01006.xen_US
dc.identifier.scopuseid_2-s2.0-84856019736en_US
dc.identifier.hkuros210023-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84856019736&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume74en_US
dc.identifier.issue1en_US
dc.identifier.spage91en_US
dc.identifier.epage109en_US
dc.identifier.eissn1467-9868-
dc.identifier.isiWOS:000299203400005-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLee, SMS=24280225500en_US

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