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

For testing two random vectors for independence, we consider testing whetherthe distance of one vector from a center point is independent from the distanceof the other vector from a center point by a univariate test. In this paper weprovide conditions under which it is enough to have a consistent univariatetest of independence on the distances to guarantee that the power to detectdependence between the random vectors increases to one, as the sample sizeincreases. These conditions turn out to be minimal. If the univariate test isdistribution-free, the multivariate test will also be distribution-free. If weconsider multiple center points and aggregate the center-specific univariatetests, the power may be further improved, and the resulting multivariate testmay be distribution-free for specific aggregation methods (if the univariatetest is distribution-free). We show that several multivariate tests recentlyproposed in the literature can be viewed as instances of this general approach.

Multivariate tests of association based on univariate tests