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Article: Minimum variance unbiased estimation based on bootstrap iterations

TitleMinimum variance unbiased estimation based on bootstrap iterations
Authors
KeywordsBias
Bootstrap Iteration
Mle
Monte Carlo
Mvue
Issue Date2006
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174
Citation
Statistics And Computing, 2006, v. 16 n. 3, p. 267-277 How to Cite?
AbstractPractical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE. Viewing bootstrap iteration as a Markov process, we develop a computational algorithm for bias correction based on arbitrarily many bootstrap iterations. The algorithm, when applied parametrically to finite sample spaces, does not involve Monte Carlo simulation. For infinite sample spaces, a nonparametric version of the algorithm is combined with a preliminary round of Monte Carlo simulation to yield an approximate MVUE. Both algorithms are computationally more efficient and stable than conventional simulation-based bootstrap iterations. Examples are given of both finite and infinite sample spaces to illustrate the effectiveness of our new approach. © Springer Science + Business Media, LLC 2006.
Persistent Identifierhttp://hdl.handle.net/10722/172422
ISSN
2015 Impact Factor: 1.786
2015 SCImago Journal Rankings: 1.993
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, KYFen_US
dc.contributor.authorLee, SMSen_US
dc.contributor.authorNg, KWen_US
dc.date.accessioned2012-10-30T06:22:25Z-
dc.date.available2012-10-30T06:22:25Z-
dc.date.issued2006en_US
dc.identifier.citationStatistics And Computing, 2006, v. 16 n. 3, p. 267-277en_US
dc.identifier.issn0960-3174en_US
dc.identifier.urihttp://hdl.handle.net/10722/172422-
dc.description.abstractPractical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE. Viewing bootstrap iteration as a Markov process, we develop a computational algorithm for bias correction based on arbitrarily many bootstrap iterations. The algorithm, when applied parametrically to finite sample spaces, does not involve Monte Carlo simulation. For infinite sample spaces, a nonparametric version of the algorithm is combined with a preliminary round of Monte Carlo simulation to yield an approximate MVUE. Both algorithms are computationally more efficient and stable than conventional simulation-based bootstrap iterations. Examples are given of both finite and infinite sample spaces to illustrate the effectiveness of our new approach. © Springer Science + Business Media, LLC 2006.en_US
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174en_US
dc.relation.ispartofStatistics and Computingen_US
dc.subjectBiasen_US
dc.subjectBootstrap Iterationen_US
dc.subjectMleen_US
dc.subjectMonte Carloen_US
dc.subjectMvueen_US
dc.titleMinimum variance unbiased estimation based on bootstrap iterationsen_US
dc.typeArticleen_US
dc.identifier.emailLee, SMS: smslee@hku.hken_US
dc.identifier.emailNg, KW: kaing@hkucc.hku.hken_US
dc.identifier.authorityLee, SMS=rp00726en_US
dc.identifier.authorityNg, KW=rp00765en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s11222-006-8078-8en_US
dc.identifier.scopuseid_2-s2.0-33745597331en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745597331&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume16en_US
dc.identifier.issue3en_US
dc.identifier.spage267en_US
dc.identifier.epage277en_US
dc.identifier.isiWOS:000238746900005-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChan, KYF=7406035182en_US
dc.identifier.scopusauthoridLee, SMS=24280225500en_US
dc.identifier.scopusauthoridNg, KW=7403178774en_US
dc.identifier.citeulike824395-

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