Assessing Monotone Dependence

The assessment of monotone dependence between random variables $X$ and $Y$ is a classical problem in statistics and a gamut of application domains. Consequently, researchers have sought measures of association that are invariant under strictly increasing transformations of the margins, with the extant literature being splintered. Rank correlation coefficients, such as Spearman's rho and Kendall's tau, have been studied at great length in the statistical literature, mostly under the assumption that $X$ and $Y$ are continuous. In the case of a dichotomous outcome $Y$, receiver operating characteristic (ROC) analysis and the asymmetric area under the ROC curve (AUC) measure are used to assess monotone dependence of $Y$ on a covariate $X$. In this talk I demonstrate that the two thus far disconnected strands of literature can be unified and bridged, by developing common population level theory, common estimators, and common tests that apply to all types of linearly ordered outcomes. In case studies, we assess progress in artificial intelligence (AI) based weather prediction and evaluate methods of uncertainty quantification for the output of large language models. The talk is based on joint work with Eva-Maria Walz and Andreas Eberl.