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arXiv · 1906.01465

Gap-Measure Tests with Applications to Data Integrity Verification

Truc Le·Jeffrey Uhlmann
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Abstract

In this paper we propose and examine gap statistics for assessing uniform distribution hypotheses. We provide examples relevant to data integrity testing for which max-gap statistics provide greater sensitivity than chi-square ($χ^2$), thus allowing the new test to be used in place of or as a complement to $χ^2$ testing for purposes of distinguishing a larger class of deviations from uniformity. We establish that the proposed max-gap test has the same sequential and parallel computational complexity as $χ^2$ and thus is applicable for Big Data analytics and integrity verification.

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Truc Le, Jeffrey Uhlmann. 2019-06-03. Gap-Measure Tests with Applications to Data Integrity Verification. https://doi.org/10.14355/srl.2015.04.003

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