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False discovery rate for scanning statistics

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  • D. O. Siegmund
  • N. R. Zhang
  • B. Yakir

Abstract

The false discovery rate is a criterion for controlling Type I error in simultaneous testing of multiple hypotheses. For scanning statistics, due to local dependence, clusters of neighbouring hypotheses are likely to be rejected together. In such situations, it is more intuitive and informative to group neighbouring rejections together and count them as a single discovery, with the false discovery rate defined as the proportion of clusters that are falsely declared among all declared clusters. Assuming that the number of false discoveries, under this broader definition of a discovery, is approximately Poisson and independent of the number of true discoveries, we examine approaches for estimating and controlling the false discovery rate, and provide examples from biological applications. Copyright 2011, Oxford University Press.

Suggested Citation

  • D. O. Siegmund & N. R. Zhang & B. Yakir, 2011. "False discovery rate for scanning statistics," Biometrika, Biometrika Trust, vol. 98(4), pages 979-985.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:4:p:979-985
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    File URL: http://hdl.handle.net/10.1093/biomet/asr057
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    Citations

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    Cited by:

    1. Reiner-Benaim Anat & Davis Ronald W. & Juneau Kara, 2014. "Scan statistics analysis for detection of introns in time-course tiling array data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 173-190, April.
    2. Page Christian M. & Harbo Hanne F. & Vos Linda & Rounge Trine B. & Andreassen Bettina K., 2018. "Assessing genome-wide significance for the detection of differentially methylated regions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-8, October.
    3. Yueying Wang & Guannan Wang & Li Wang & R. Todd Ogden, 2020. "Simultaneous confidence corridors for mean functions in functional data analysis of imaging data," Biometrics, The International Biometric Society, vol. 76(2), pages 427-437, June.
    4. Anat Reiner-Benaim, 2016. "Scan Statistic Tail Probability Assessment Based on Process Covariance and Window Size," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 717-745, September.

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