Gap-Measure Tests with Applications to Data Integrity Verification
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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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