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On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality

Michael Naaman

Statistics & Probability Letters, 2021, vol. 173, issue C

Abstract: We derive the tight constant in the multivariate version of the Dvoretzky–Kiefer–Wolfowitz inequality. The inequality is leveraged to construct the first fully non-parametric test for multivariate probability distributions including a simple formula for the test statistic. We also generalize the test under appropriate α-mixing conditions and describe applications of the tests to machine learning and representative sampling.

Keywords: Machine learning; Empirical process; Hypothesis test; Non-parametric (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spl.2021.109088

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