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Statistical properties of an early stopping rule for resampling-based multiple testing

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  • Hui Jiang
  • Julia Salzman

Abstract

Resampling-based methods for multiple hypothesis testing often lead to long run times when the number of tests is large. This paper presents a simple rule that substantially reduces computation by allowing resampling to terminate early on a subset of tests. We prove that the method has a low probability of obtaining a set of rejected hypotheses different from those rejected without early stopping, and obtain error bounds for multiple hypothesis testing. Simulation shows that our approach saves more computation than other available procedures. Copyright 2012, Oxford University Press.

Suggested Citation

  • Hui Jiang & Julia Salzman, 2012. "Statistical properties of an early stopping rule for resampling-based multiple testing," Biometrika, Biometrika Trust, vol. 99(4), pages 973-980.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:4:p:973-980
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    File URL: http://hdl.handle.net/10.1093/biomet/ass051
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    Cited by:

    1. Wei Sun & Yufeng Liu & James J. Crowley & Ting-Huei Chen & Hua Zhou & Haitao Chu & Shunping Huang & Pei-Fen Kuan & Yuan Li & Darla Miller & Ginger Shaw & Yichao Wu & Vasyl Zhabotynsky & Leonard McMill, 2015. "IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 975-986, September.
    2. Brian D. Segal & Thomas Braun & Michael R. Elliott & Hui Jiang, 2018. "Fast approximation of small p†values in permutation tests by partitioning the permutations," Biometrics, The International Biometric Society, vol. 74(1), pages 196-206, March.
    3. Axel Gandy & Georg Hahn, 2016. "A Framework for Monte Carlo based Multiple Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1046-1063, December.
    4. Yang Shi & Hui Jiang, 2013. "rSeqDiff: Detecting Differential Isoform Expression from RNA-Seq Data Using Hierarchical Likelihood Ratio Test," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-11, November.
    5. Axel Gandy & Georg Hahn, 2014. "MMCTest—A Safe Algorithm for Implementing Multiple Monte Carlo Tests," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1083-1101, December.

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