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Article: Prepivoting by weighted bootstrap iteration

TitlePrepivoting by weighted bootstrap iteration
Authors
KeywordsBootstrap iteration
Linear regression
M-estimation
Pivot
Prepivoting
Smooth function model
Weighted bootstrap iteration
Issue Date2003
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2003, v. 90 n. 2, p. 393-410 How to Cite?
AbstractPrepivoting by conventional bootstrap iteration is known to yield a progressively more accurate pivot in certain problems, and has important application in the construction of confidence limits and estimation of null distributions. We investigate the theoretical effects of weighted bootstrap iteration on prepivoting and show that each weighted bootstrap iteration, with weights chosen carefully but empirically, is asymptotically equivalent to two consecutive conventional bootstrap iterations. In terms of reducing the order of error, prepivoting can therefore be carried out much more efficiently if based on weighted bootstrap iterations. This is shown for a variety of problem settings, including the smooth function model, M-estimation and the regression context. A numerical illustration is provided, demonstrating the potential practical usefulness of weighted prepivoting.
Persistent Identifierhttp://hdl.handle.net/10722/82729
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLee, SMSen_HK
dc.contributor.authorYoung, GAen_HK
dc.date.accessioned2010-09-06T08:32:44Z-
dc.date.available2010-09-06T08:32:44Z-
dc.date.issued2003en_HK
dc.identifier.citationBiometrika, 2003, v. 90 n. 2, p. 393-410en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82729-
dc.description.abstractPrepivoting by conventional bootstrap iteration is known to yield a progressively more accurate pivot in certain problems, and has important application in the construction of confidence limits and estimation of null distributions. We investigate the theoretical effects of weighted bootstrap iteration on prepivoting and show that each weighted bootstrap iteration, with weights chosen carefully but empirically, is asymptotically equivalent to two consecutive conventional bootstrap iterations. In terms of reducing the order of error, prepivoting can therefore be carried out much more efficiently if based on weighted bootstrap iterations. This is shown for a variety of problem settings, including the smooth function model, M-estimation and the regression context. A numerical illustration is provided, demonstrating the potential practical usefulness of weighted prepivoting.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectBootstrap iterationen_HK
dc.subjectLinear regressionen_HK
dc.subjectM-estimationen_HK
dc.subjectPivoten_HK
dc.subjectPrepivotingen_HK
dc.subjectSmooth function modelen_HK
dc.subjectWeighted bootstrap iterationen_HK
dc.titlePrepivoting by weighted bootstrap iterationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=90&issue=2&spage=393&epage=410&date=2003&atitle=Prepivoting+by+weighted+bootstrap+iterationen_HK
dc.identifier.emailLee, SMS: smslee@hku.hken_HK
dc.identifier.authorityLee, SMS=rp00726en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/90.2.393en_HK
dc.identifier.scopuseid_2-s2.0-0347556775en_HK
dc.identifier.hkuros88923en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0347556775&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume90en_HK
dc.identifier.issue2en_HK
dc.identifier.spage393en_HK
dc.identifier.epage410en_HK
dc.identifier.isiWOS:000183818500011-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK
dc.identifier.scopusauthoridYoung, GA=36723800600en_HK

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