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A framework for paired-sample hypothesis testing for high-dimensional data
First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator.
Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning
In this work, we present the ts-AUC, a non-parametric multivariate two-sample test, which we employ to analyze statokinesigram differences among PS patients that are fallers (PSf) and non-fallers (PSNF).
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