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Found 4 papers, 2 papers with code
On Ranking-based Tests of Independence
In this paper we develop a novel nonparametric framework to test the independence of two random variables $\mathbf{X}$ and $\mathbf{Y}$ with unknown respective marginals $H(dx)$ and $G(dy)$ and joint distribution $F(dx dy)$, based on {\it Receiver Operating Characteristic} (ROC) analysis and bipartite ranking.
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics
The ability to collect and store ever more massive databases has been accompanied by the need to process them efficiently.
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking
The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis.
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).
