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Scan clustering: A false discovery approach

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  • Perone Pacifico, M.
  • Genovese, C.
  • Verdinelli, I.
  • Wasserman, L.

Abstract

We present a method that scans a random field for localized clusters while controlling the fraction of false discoveries. We use a kernel density estimator as the test statistic and adjust for the bias in this estimator by a method we introduce in this paper. We also show how to combine information across multiple bandwidths while maintaining false discovery control.

Suggested Citation

  • Perone Pacifico, M. & Genovese, C. & Verdinelli, I. & Wasserman, L., 2007. "Scan clustering: A false discovery approach," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1441-1469, August.
    • Handle: RePEc:eee:jmvana:v:98:y:2007:i:7:p:1441-1469
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    References listed on IDEAS

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    1. Cristóbal, J. A. & Alcalá, J. T., 1998. "Error Process Indexed by Bandwidth Matrices in Multivariate Local Linear Smoothing," Journal of Multivariate Analysis, Elsevier, vol. 66(2), pages 207-236, August.
    2. M. Perone Pacifico & C. Genovese & I. Verdinelli & L. Wasserman, 2004. "False Discovery Control for Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1002-1014, December.
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    Cited by:

    1. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
    2. Anthony Cheng & Disheng Mao & Yuping Zhang & Joseph Glaz & Zhengqing Ouyang, 2023. "Translocation detection from Hi‐C data via scan statistics," Biometrics, The International Biometric Society, vol. 79(2), pages 1306-1317, June.
    3. 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.

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