6 papers
Estimating the Hüsler--Reiss variogram matrix by clipped moments
Shuang Hu, Johan Segers
In multivariate extreme value analysis, the tail dependence between some of the risk variables at hand may be weak, even when other variables do tend to become large simultaneously…
Directional variograms for multivariate extremes
Manuel Hentschel, Frank Röttger, Johan Segers +1
Multivariate generalized Pareto distributions arise as limits of threshold exceedances and form a central model class for multivariate extremes. Existing inference methods based on…
A penalized least squares estimator for extreme-value mixture models
Anas Mourahib, Anna Kiriliouk, Johan Segers
Estimating the parameters of max-stable parametric models poses significant challenges, particularly when some parameters lie on the boundary of the parameter space. This situation…
Tail calibration of probabilistic forecasts
Sam Allen, Jonathan Koh, Johan Segers +1
Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods…
X-Vine Models for Multivariate Extremes
Anna Kiriliouk, Jeongjin Lee, Johan Segers
Regular vine sequences permit the organisation of variables in a random vector along a sequence of trees. Regular vine models have become greatly popular in dependence modelling as…
Multivariate generalized Pareto distributions along extreme directions
Anas Mourahib, Anna Kiriliouk, Johan Segers
When modeling a vector of risk variables, extreme scenarios are often of special interest. The peaks-over-thresholds method hinges on the notion that, asymptotically, the excesses…