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Extreme Value Theory for Binary Expansion Testing

Author

Listed:
  • Siqi Xiang

    (University of North Carolina at Chapel Hill)

  • Wan Zhang

    (University of North Carolina at Chapel Hill)

  • Kai Zhang

    (University of North Carolina at Chapel Hill)

  • J. S. Marron

    (University of North Carolina at Chapel Hill)

Abstract

Binary expansion testing (BET) provides powerful detection of interesting nonlinear dependence among pairs of variables in the exploratory data analysis of large-scale data sets. However, the Bonferroni adjusted p-values can be overly conservative when used to determine the significant testing pairs. A novel contribution of this paper is the extreme value theory analysis of BET. This results in a potentially powerful new significance threshold for the maximal BET z-statistics.

Suggested Citation

  • Siqi Xiang & Wan Zhang & Kai Zhang & J. S. Marron, 2024. "Extreme Value Theory for Binary Expansion Testing," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 327-343, November.
    • Handle: RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-023-00333-7
    DOI: 10.1007/s13171-023-00333-7
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