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Article: An exact iterated bootstrap algorithm for small-sample bias reduction

TitleAn exact iterated bootstrap algorithm for small-sample bias reduction
Authors
KeywordsBias reduction
Boostrap iteration
Markov chain
Monte Carlo
Issue Date2001
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2001, v. 36 n. 1, p. 1-13 How to Cite?
AbstractIt is well known that bootstrap accuracy can be theoretically enhanced by iterating the bootstrap procedure. Monte Carlo approximation to bootstrap iteration incurs prohibitively expensive computational cost, especially when higher levels of resampling are involved. The theoretical gain promised by high-level bootstrap iteration can thus hardly be materialized in practice. By considering bootstrap iteration as a Markov process, we propose an algorithm for its implementation in the context of small-sample bias reduction. The algorithm caters for any number of bootstrap iterations and computes exact bootstrap bias-corrected estimates without the need for extensive Monte Carlo resampling. We discuss the practical value of our algorithm in situations where infinite-level bootstrap iteration yields an unbiased estimate irrespective of the sample size and where our algorithm converges rapidly. Numerical examples are given to illustrate applications to estimates such as functions of sample means, sample quantiles and the Nadaraya-Watson estimate. © 2001 Elsevier Science B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/82795
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
References

 

DC FieldValueLanguage
dc.contributor.authorChan, KYFen_HK
dc.contributor.authorLee, SMSen_HK
dc.date.accessioned2010-09-06T08:33:30Z-
dc.date.available2010-09-06T08:33:30Z-
dc.date.issued2001en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2001, v. 36 n. 1, p. 1-13en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82795-
dc.description.abstractIt is well known that bootstrap accuracy can be theoretically enhanced by iterating the bootstrap procedure. Monte Carlo approximation to bootstrap iteration incurs prohibitively expensive computational cost, especially when higher levels of resampling are involved. The theoretical gain promised by high-level bootstrap iteration can thus hardly be materialized in practice. By considering bootstrap iteration as a Markov process, we propose an algorithm for its implementation in the context of small-sample bias reduction. The algorithm caters for any number of bootstrap iterations and computes exact bootstrap bias-corrected estimates without the need for extensive Monte Carlo resampling. We discuss the practical value of our algorithm in situations where infinite-level bootstrap iteration yields an unbiased estimate irrespective of the sample size and where our algorithm converges rapidly. Numerical examples are given to illustrate applications to estimates such as functions of sample means, sample quantiles and the Nadaraya-Watson estimate. © 2001 Elsevier Science 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/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.rightsComputational Statistics & Data Analysis. Copyright © Elsevier BV.en_HK
dc.subjectBias reductionen_HK
dc.subjectBoostrap iterationen_HK
dc.subjectMarkov chainen_HK
dc.subjectMonte Carloen_HK
dc.titleAn exact iterated bootstrap algorithm for small-sample bias reductionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=36&spage=1&epage=13&date=2001&atitle=An+exact+iterated+bootstrap+algorithm+for+small-sample+bias+reductionen_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/S0167-9473(00)00029-3en_HK
dc.identifier.scopuseid_2-s2.0-0035962006en_HK
dc.identifier.hkuros62106en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035962006&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume36en_HK
dc.identifier.issue1en_HK
dc.identifier.spage1en_HK
dc.identifier.epage13en_HK
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridChan, KYF=7406035182en_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK

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