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Article: Parametric bootstrapping with nuisance parameters

TitleParametric bootstrapping with nuisance parameters
Authors
KeywordsBootstrap
Confidence set
Constrained maximum likelihood estimator
Global maximum likelihood estimator
Parametric bootstrap
Prepivoting
Issue Date2005
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/stapro
Citation
Statistics And Probability Letters, 2005, v. 71 n. 2, p. 143-153 How to Cite?
AbstractBootstrap methods are attractive empirical procedures for assessment of errors in problems of statistical estimation, and allow highly accurate inference in a vast range of parametric problems. Conventional parametric bootstrapping involves sampling from a fitted parametric model, obtained by substituting the maximum likelihood estimator for the unknown population parameter. Recently, attention has focussed on modified bootstrap methods which alter the sampling model used in the bootstrap calculation, in a systematic way that is dependent on the parameter of interest. Typically, inference is required for the interest parameter in the presence of a nuisance parameter, in which case the issue of how best to handle the nuisance parameter in the bootstrap inference arises. In this paper, we provide a general analysis of the error reduction properties of the parametric bootstrap. We show that conventional parametric bootstrapping succeeds in reducing error quite generally, when applied to an asymptotically normal pivot, and demonstrate further that systematic improvements are obtained by a particular form of modified scheme, in which the nuisance parameter is substituted by its constrained maximum likelihood estimator, for a given value of the parameter of interest. © 2004 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/83044
ISSN
2015 Impact Factor: 0.506
2015 SCImago Journal Rankings: 0.720
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLee, SMSen_HK
dc.contributor.authorYoung, GAen_HK
dc.date.accessioned2010-09-06T08:36:17Z-
dc.date.available2010-09-06T08:36:17Z-
dc.date.issued2005en_HK
dc.identifier.citationStatistics And Probability Letters, 2005, v. 71 n. 2, p. 143-153en_HK
dc.identifier.issn0167-7152en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83044-
dc.description.abstractBootstrap methods are attractive empirical procedures for assessment of errors in problems of statistical estimation, and allow highly accurate inference in a vast range of parametric problems. Conventional parametric bootstrapping involves sampling from a fitted parametric model, obtained by substituting the maximum likelihood estimator for the unknown population parameter. Recently, attention has focussed on modified bootstrap methods which alter the sampling model used in the bootstrap calculation, in a systematic way that is dependent on the parameter of interest. Typically, inference is required for the interest parameter in the presence of a nuisance parameter, in which case the issue of how best to handle the nuisance parameter in the bootstrap inference arises. In this paper, we provide a general analysis of the error reduction properties of the parametric bootstrap. We show that conventional parametric bootstrapping succeeds in reducing error quite generally, when applied to an asymptotically normal pivot, and demonstrate further that systematic improvements are obtained by a particular form of modified scheme, in which the nuisance parameter is substituted by its constrained maximum likelihood estimator, for a given value of the parameter of interest. © 2004 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/staproen_HK
dc.relation.ispartofStatistics and Probability Lettersen_HK
dc.rightsStatistics & Probability Letters. Copyright © Elsevier BV.en_HK
dc.subjectBootstrapen_HK
dc.subjectConfidence seten_HK
dc.subjectConstrained maximum likelihood estimatoren_HK
dc.subjectGlobal maximum likelihood estimatoren_HK
dc.subjectParametric bootstrapen_HK
dc.subjectPrepivotingen_HK
dc.titleParametric bootstrapping with nuisance parametersen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-7152&volume=71&spage=143&epage=153&date=2005&atitle=Parametric+bootstrapping+with+nuisance+parametersen_HK
dc.identifier.emailLee, SMS: smslee@hku.hken_HK
dc.identifier.authorityLee, SMS=rp00726en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.spl.2004.10.026en_HK
dc.identifier.scopuseid_2-s2.0-13744258875en_HK
dc.identifier.hkuros100408en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-13744258875&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume71en_HK
dc.identifier.issue2en_HK
dc.identifier.spage143en_HK
dc.identifier.epage153en_HK
dc.identifier.isiWOS:000227498300004-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK
dc.identifier.scopusauthoridYoung, GA=36723800600en_HK

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