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Article: Double block bootstrap confidence intervals for dependent data

TitleDouble block bootstrap confidence intervals for dependent data
Authors
KeywordsCoverage calibration
Double block bootstrap
P-value
Studentization
Weakly dependent
Issue Date2009
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2009, v. 96 n. 2, p. 427-443 How to Cite?
AbstractThe block bootstrap confidence interval for dependent data can outperform the conventional normal approximation only with nontrivial studentization which, in the case of complicated statistics, calls for specialist treatment and often results in unstable endpoints. We propose two double block bootstrap approaches for improving the accuracy of the block bootstrap confidence interval under very general conditions. The first approach calibrates the nominal coverage level and the second calculates studentizing factors directly from a block bootstrap series without the need for nontrivial analytical treatment. We prove that the two approaches reduce the coverage error of the block bootstrap interval by an order of magnitude with simple tuning of block lengths at the two block bootstrapping levels. Empirical properties of the procedures are investigated by simulations and application to an econometric time series. © 2009 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/59867
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong Special Administrative Region, China
Funding Information:

This work was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China. We would like to thank the referees and Professor D. M. Titterington for their constructive comments. Finally, we would like to thank one referee for providing the coverage results for I<INF>GK</INF>-Q<INF>S</INF> in Figs. 2(a) and (b), which were based on a larger simulation size of 10 000 Monte Carlo replications.

References

 

DC FieldValueLanguage
dc.contributor.authorLee, SMSen_HK
dc.contributor.authorLai, PYen_HK
dc.date.accessioned2010-05-31T03:59:04Z-
dc.date.available2010-05-31T03:59:04Z-
dc.date.issued2009en_HK
dc.identifier.citationBiometrika, 2009, v. 96 n. 2, p. 427-443en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59867-
dc.description.abstractThe block bootstrap confidence interval for dependent data can outperform the conventional normal approximation only with nontrivial studentization which, in the case of complicated statistics, calls for specialist treatment and often results in unstable endpoints. We propose two double block bootstrap approaches for improving the accuracy of the block bootstrap confidence interval under very general conditions. The first approach calibrates the nominal coverage level and the second calculates studentizing factors directly from a block bootstrap series without the need for nontrivial analytical treatment. We prove that the two approaches reduce the coverage error of the block bootstrap interval by an order of magnitude with simple tuning of block lengths at the two block bootstrapping levels. Empirical properties of the procedures are investigated by simulations and application to an econometric time series. © 2009 Biometrika Trust.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.subjectCoverage calibrationen_HK
dc.subjectDouble block bootstrapen_HK
dc.subjectP-valueen_HK
dc.subjectStudentizationen_HK
dc.subjectWeakly dependenten_HK
dc.titleDouble block bootstrap confidence intervals for dependent dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=96&issue=2&spage=427&epage=443&date=2009&atitle=Double+block+bootstrap+confidence+intervals+for+dependent+dataen_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/asp018en_HK
dc.identifier.scopuseid_2-s2.0-66249121016en_HK
dc.identifier.hkuros163465en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-66249121016&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume96en_HK
dc.identifier.issue2en_HK
dc.identifier.spage427en_HK
dc.identifier.epage443en_HK
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000266344300013-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK
dc.identifier.scopusauthoridLai, PY=8629588700en_HK
dc.identifier.citeulike5661366-

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