Extreme Value Theory for Binary Expansion Testing

Siqi Xiang (), Wan Zhang (), Kai Zhang () and J. S. Marron ()
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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

Sankhya A: The Indian Journal of Statistics, 2024, vol. 86, issue 1, No 17, 327-343

Abstract: 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.

Keywords: Binary expansion testing; extreme value; nonlinear dependence; nonparametric dependence testing; Primary 62G32; Secondary 62G10 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13171-023-00333-7

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