English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Kendall plot ($\K$-plot) is a plot measuring dependence between thecomponents of a bivariate random variable. The $\K$-plot graphs the Kendalldistribution function against the distribution function of $VU$, where $V$ and$U$ are independent uniform $[0,1]$ random variables. We associate $\K$-plotswith the receiver operating characteristic ($\ROC$) curve, a well-acceptedgraphical tool in biostatistics for evaluating the ability of a biomarker todiscriminate between two populations. The most commonly used global index ofdiagnostic accuracy of biomarkers is the area under the $\ROC$ curve ($\AUC$).In parallel with the $\AUC$, we propose a novel strategy to measure theassociation between random variables from a continuous bivariate distribution.First, we discuss why the area under the conventional Kendall curve ($\AUK$)cannot be used as an index of dependence. We then suggest a simple andmeaningful extension of the definition of the $\K$-plots and define an index ofdependence that is based on $\AUK$. This measure characterizes a wide range oftwo-variable relationships, thereby completely detecting the underlyingdependence structure. Properties of the proposed index satisfy the mathematicaldefinition of a measure. Finally, simulations and real data examples illustratethe applicability of the proposed method.

Multi-Panel Kendall Plot in Light of an ROC Curve Analysis Applied to Measuring Dependence