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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Let $(X_1,Y_1),\ldots,(X_n,Y_n)$ be an i.i.d. sample from a bivariatedistribution function that lies in the max-domain of attraction of an extremevalue distribution. The asymptotic joint distribution of the standardizedcomponent-wise maxima $\bigvee_{i=1}^nX_i$ and $\bigvee_{i=1}^nY_i$ is thencharacterized by the marginal extreme value indices and the tail copula $R$. Wepropose a procedure for constructing asymptotically distribution-freegoodness-of-fit tests for the tail copula $R$. The procedure is based on atransformation of a suitable empirical process derived from a semi-parametricestimator of $R$. The transformed empirical process converges weakly to astandard Wiener process, paving the way for a multitude of asymptoticallydistribution-free goodness-of-fit tests. We also extend our results to the$m$-variate ($m>2$) case. In a simulation study we show that the limit theoremsprovide good approximations for finite samples and that tests based on thetransformed empirical process have high power.

Asymptotically distribution-free goodness-of-fit testing for tail copulas