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    Efficient p-value evaluation for resampling-based tests

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    Type
    Article
    Authors
    Yu, K.
    Liang, F.
    Ciampa, J.
    Chatterjee, N.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2011-01-05
    Online Publication Date
    2011-01-05
    Print Publication Date
    2011-07-01
    Permanent link to this record
    http://hdl.handle.net/10754/598113
    
    Metadata
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    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.
    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.
    Publisher
    Oxford University Press (OUP)
    Journal
    Biostatistics
    DOI
    10.1093/biostatistics/kxq078
    PubMed ID
    21209154
    PubMed Central ID
    PMC3114653
    ae974a485f413a2113503eed53cd6c53
    10.1093/biostatistics/kxq078
    Scopus Count
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