KAUST Grant NumberKUS-C1-016-04
MetadataShow full item record
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.
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.
SponsorsIntramural 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.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3114653
CollectionsPublications Acknowledging KAUST Support
- Efficient Monte Carlo evaluation of resampling-based hypothesis tests with applications to genetic epidemiology.
- Authors: Fung WK, Yu K, Yang Y, Zhou JY
- Issue date: 2018 May
- Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study.
- Authors: Zhang X, Huang S, Sun W, Wang W
- Issue date: 2012 Apr
- MAX-rank: a simple and robust genome-wide scan for case-control association studies.
- Authors: Li Q, Yu K, Li Z, Zheng G
- Issue date: 2008 Jul
- An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait Loci.
- Authors: Zou F, Fine JP, Hu J, Lin DY
- Issue date: 2004 Dec
- Fast approximation of small p-values in permutation tests by partitioning the permutations.
- Authors: Segal BD, Braun T, Elliott MR, Jiang H
- Issue date: 2018 Mar