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Article: On a class of m out of n bootstrap confidence intervals

TitleOn a class of m out of n bootstrap confidence intervals
Authors
KeywordsCoverage
Double bootstrap
m out of n bootstrap
Smooth function model
Issue Date1999
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, 1999, v. 61 n. 4, p. 901-911 How to Cite?
AbstractIt is widely known that bootstrap failure can often be remedied by using a technique known as the 'm out of n' bootstrap, by which a smaller number, m say, of observations are resampled from the original sample of size n. In successful cases of the bootstrap, the m out of n bootstrap is often deemed unnecessary. We show that the problem of constructing nonparametric confidence intervals is an exceptional case. By considering a new class of m out of n bootstrap confidence limits, we develop a computationally efficient approach based on the double bootstrap to construct the optimal m out of n bootstrap intervals. We show that the optimal intervals have a coverage accuracy which is comparable with that of the classical double-bootstrap intervals, and we conduct a simulation study to examine their performance. The results are in general very encouraging. Alternative approaches which yield even higher order accuracy are also discussed.
Persistent Identifierhttp://hdl.handle.net/10722/83033
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
References

 

DC FieldValueLanguage
dc.contributor.authorLee, SMSen_HK
dc.date.accessioned2010-09-06T08:36:10Z-
dc.date.available2010-09-06T08:36:10Z-
dc.date.issued1999en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 1999, v. 61 n. 4, p. 901-911en_HK
dc.identifier.issn1369-7412en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83033-
dc.description.abstractIt is widely known that bootstrap failure can often be remedied by using a technique known as the 'm out of n' bootstrap, by which a smaller number, m say, of observations are resampled from the original sample of size n. In successful cases of the bootstrap, the m out of n bootstrap is often deemed unnecessary. We show that the problem of constructing nonparametric confidence intervals is an exceptional case. By considering a new class of m out of n bootstrap confidence limits, we develop a computationally efficient approach based on the double bootstrap to construct the optimal m out of n bootstrap intervals. We show that the optimal intervals have a coverage accuracy which is comparable with that of the classical double-bootstrap intervals, and we conduct a simulation study to examine their performance. The results are in general very encouraging. Alternative approaches which yield even higher order accuracy are also discussed.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_HK
dc.subjectCoverageen_HK
dc.subjectDouble bootstrapen_HK
dc.subjectm out of n bootstrapen_HK
dc.subjectSmooth function modelen_HK
dc.titleOn a class of m out of n bootstrap confidence intervalsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0964-1998&volume=61&spage=901&epage=911&date=1999&atitle=On+a+class+of+m+out+of+n+bootstrap+confidence+intervalsen_HK
dc.identifier.emailLee, SMS: smslee@hku.hken_HK
dc.identifier.authorityLee, SMS=rp00726en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0033475168en_HK
dc.identifier.hkuros62173en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033475168&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue4en_HK
dc.identifier.spage901en_HK
dc.identifier.epage911en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK

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