Efficient p-value evaluation for resampling-based tests.
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...
| Publicado en: | Biostatistics Vol. 12; no. 3; pp. 582 - 594 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104646118&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104646118 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Jul2011 vid: 12 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104646118 104646118 NLM21209154 2011175377 10.1093/biostatistics/kxq078 NLM21209154 PMC3114653 104646118 ppf: 582 ppct: 12 formats: tig: atl: Efficient p-value evaluation for resampling-based tests. aug: au: Yu K Liang F Ciampa J Chatterjee N Yu, Kai Liang, Faming Ciampa, Julia Chatterjee, Nilanjan affil: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA sug: subj: Algorithms Data Analysis, Statistical Systems Analysis Statistics Computer Simulation Sequence Analysis Methods Male Polymorphism, Genetic Prostatic Neoplasms Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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