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Article: Bootstrap estimation of actual significance levels for tests based on estimated nuisance parameters

TitleBootstrap estimation of actual significance levels for tests based on estimated nuisance parameters
Authors
KeywordsBootstrap estimation
Extreme value
Hypothesis test
Non-regular parametric family
Nuisance parameter
Issue Date2002
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, 2002, v. 11, p. 367-371 How to Cite?
AbstractOften for a non-regular parametric hypothesis, a tractable test statistic involves a nuisance parameter. A common practice is to replace the unknown nuisance parameter by its estimator. The validality of such a replacement can only be justified for an infinite sample in the sense that under appropriate conditions the asymptotic distribution of the statistic under the null hypothesis is unchanged when the nuisance parameter is replaced by its estimator (Crowder M.J. 1990. Biometrika 77: 499–506). We propose a bootstrap method to calibrate the error incurred in the significance level, for finite samples, due to the replacement. Further, we have proved that the bootstrap method provides a more accurate estimator for the unknown actual significance level than the nominal level. Simulations demonstrate the proposed methodology.
Persistent Identifierhttp://hdl.handle.net/10722/224529
ISSN
2015 Impact Factor: 1.786
2015 SCImago Journal Rankings: 1.993

 

DC FieldValueLanguage
dc.contributor.authorYao, Q-
dc.contributor.authorZhang, W-
dc.contributor.authorTong, H-
dc.date.accessioned2016-04-07T01:49:46Z-
dc.date.available2016-04-07T01:49:46Z-
dc.date.issued2002-
dc.identifier.citationStatistics and Computing, 2002, v. 11, p. 367-371-
dc.identifier.issn0960-3174-
dc.identifier.urihttp://hdl.handle.net/10722/224529-
dc.description.abstractOften for a non-regular parametric hypothesis, a tractable test statistic involves a nuisance parameter. A common practice is to replace the unknown nuisance parameter by its estimator. The validality of such a replacement can only be justified for an infinite sample in the sense that under appropriate conditions the asymptotic distribution of the statistic under the null hypothesis is unchanged when the nuisance parameter is replaced by its estimator (Crowder M.J. 1990. Biometrika 77: 499–506). We propose a bootstrap method to calibrate the error incurred in the significance level, for finite samples, due to the replacement. Further, we have proved that the bootstrap method provides a more accurate estimator for the unknown actual significance level than the nominal level. Simulations demonstrate the proposed methodology.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174-
dc.relation.ispartofStatistics and Computing-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1023/A:1011977221590-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBootstrap estimation-
dc.subjectExtreme value-
dc.subjectHypothesis test-
dc.subjectNon-regular parametric family-
dc.subjectNuisance parameter-
dc.titleBootstrap estimation of actual significance levels for tests based on estimated nuisance parameters-
dc.typeArticle-
dc.identifier.emailTong, H: howell.tong@gmail.com-
dc.description.naturepostprint-
dc.identifier.doi10.1023/A:1011977221590-
dc.identifier.hkuros66715-
dc.identifier.volume11-
dc.identifier.spage367-
dc.identifier.epage371-
dc.publisher.placeUnited States-

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