Handle URI:
http://hdl.handle.net/10754/598113
Title:
Efficient p-value evaluation for resampling-based tests
Authors:
Yu, K.; Liang, F.; Ciampa, J.; Chatterjee, N.
Abstract:
The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very computationally intensive. Here, we propose an efficient p-value evaluation procedure by adapting the stochastic approximation Markov chain Monte Carlo algorithm. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be 100-500 000 times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10( - 6)). With its computational burden reduced by this proposed procedure, the versatile resampling-based test would become computationally feasible for a much wider range of applications. We demonstrate the application of the new method by applying it to a large-scale genetic association study of prostate cancer.
Citation:
Yu K, Liang F, Ciampa J, Chatterjee N (2011) Efficient p-value evaluation for resampling-based tests. Biostatistics 12: 582–593. Available: http://dx.doi.org/10.1093/biostatistics/kxq078.
Publisher:
Oxford University Press (OUP)
Journal:
Biostatistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
5-Jan-2011
DOI:
10.1093/biostatistics/kxq078
PubMed ID:
21209154
PubMed Central ID:
PMC3114653
Type:
Article
ISSN:
1465-4644; 1468-4357
Sponsors:
Intramural Program of the National Institutes of Health and the National Cancer Institute to K.Y. and F.L.; The National Science Foundation (DMS-0607755, CMMI-0926803); and the award (KUS-C1-016-04) made by the King Abdullah University of Science and Technology.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorYu, K.en
dc.contributor.authorLiang, F.en
dc.contributor.authorCiampa, J.en
dc.contributor.authorChatterjee, N.en
dc.date.accessioned2016-02-25T13:12:56Zen
dc.date.available2016-02-25T13:12:56Zen
dc.date.issued2011-01-05en
dc.identifier.citationYu K, Liang F, Ciampa J, Chatterjee N (2011) Efficient p-value evaluation for resampling-based tests. Biostatistics 12: 582–593. Available: http://dx.doi.org/10.1093/biostatistics/kxq078.en
dc.identifier.issn1465-4644en
dc.identifier.issn1468-4357en
dc.identifier.pmid21209154en
dc.identifier.doi10.1093/biostatistics/kxq078en
dc.identifier.urihttp://hdl.handle.net/10754/598113en
dc.description.abstractThe resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very computationally intensive. Here, we propose an efficient p-value evaluation procedure by adapting the stochastic approximation Markov chain Monte Carlo algorithm. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be 100-500 000 times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10( - 6)). With its computational burden reduced by this proposed procedure, the versatile resampling-based test would become computationally feasible for a much wider range of applications. We demonstrate the application of the new method by applying it to a large-scale genetic association study of prostate cancer.en
dc.description.sponsorshipIntramural Program of the National Institutes of Health and the National Cancer Institute to K.Y. and F.L.; The National Science Foundation (DMS-0607755, CMMI-0926803); and the award (KUS-C1-016-04) made by the King Abdullah University of Science and Technology.en
dc.publisherOxford University Press (OUP)en
dc.subjectBootstrap proceduresen
dc.subjectGenetic association studiesen
dc.subjectp-valueen
dc.subjectResampling-based testsen
dc.subjectStochastic approximation Markov chain Monte Carloen
dc.subject.meshData Interpretation, Statisticalen
dc.subject.meshMonte Carlo Methoden
dc.subject.meshStochastic Processesen
dc.subject.meshAlgorithmsen
dc.titleEfficient p-value evaluation for resampling-based testsen
dc.typeArticleen
dc.identifier.journalBiostatisticsen
dc.identifier.pmcidPMC3114653en
dc.contributor.institutionDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA. yuka@mail.nih.goven
kaust.grant.numberKUS-C1-016-04en
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