83 citations · 404 across the 14 of their papers we have counts for
20 papers
Trends in tail dependence of heteroscedastic extremes
John H. J. Einmahl, Chen Zhou
We consider multivariate extreme value statistics for independent but nonidentically distributed random vectors. In particular, the data may have varying tail copulas and also hete…
Testing for Conditional Independence in Binary Single-Index Models
John H. J. Einmahl, Denis Kojevnikov, Bas J. M. Werker
We wish to test whether a real-valued variable has explanatory power, in addition to a multivariate variable , for a binary variable . Thus, we are interested in testing…
Cube root weak convergence of empirical estimators of a density level set
Philippe Berthet, John H. J. Einmahl
Given independent random vectors with common density on , we study the weak convergence of three empirical-measure based estimators of the convex -level se…
Spatial dependence and space-time trend in extreme events
John H. J. Einmahl, Ana Ferreira, Laurens de Haan +2
The statistical theory of extremes is extended to observations that are non-stationary and not independent. The non-stationarity over time and space is controlled via the scedasis…
Empirical tail copulas for functional data
John H. J. Einmahl, Johan Segers
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the…
Improved estimation of the extreme value index using related variables
Hanan Ahmed, John H. J. Einmahl
Heavy tailed phenomena are naturally analyzed by extreme value statistics. A crucial step in such an analysis is the estimation of the extreme value index, which describes the tail…