34 citations · 41 across the 8 of their papers we have counts for
5 papers · 1 filter
Generalizing the log-Moyal distribution and regression models for heavy tailed loss data
Zhengxiao Li, Jan Beirlant, Shengwang Meng
Catastrophic loss data are known to be heavy-tailed. Practitioners then need models that are able to capture both tail and modal parts of claim data. To this purpose, a new paramet…
Center-outward quantiles and the measurement of multivariate risk
Jan Beirlant, Sven Buitendag, Eustasio del Bario +1
All multivariate extensions of the univariate theory of risk measurement run into the same fundamental problem of the absence, in dimension d > 1, of a canonical ordering of Rd. Ba…
Outlier detection and a tail-adjusted boxplot based on extreme value theory
Shrijita Bhattacharya, Jan Beirlant
Whether an extreme observation is an outlier or not, depends strongly on the corresponding tail behaviour of the underlying distribution. We develop an automatic, data-driven metho…
Combined Tail Estimation Using Censored Data and Expert Information
Martin Bladt, Hansjoerg Albrecher, Jan Beirlant
We study tail estimation in Pareto-like settings for datasets with a high percentage of randomly right-censored data, and where some expert information on the tail index is availab…
Threshold selection and trimming in extremes
Martin Bladt, Hansjoerg Albrecher, Jan Beirlant
We consider removing lower order statistics from the classical Hill estimator in extreme value statistics, and compensating for it by rescaling the remaining terms. Trajectories of…