34 citations · 41 across the 7 of their papers we have counts for
6 papers · 1 filter
A new class of copula regression models for modelling multivariate heavy-tailed data
Zhengxiao Li, Jan Beirlant, Liang Yang
A new class of copulas, termed the MGL copula class, is introduced. The new copula originates from extracting the dependence function of the multivariate generalized log-Moyal-gamm…
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…
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…
Bias Reduced Peaks over Threshold Tail Estimation
Jan Beirlant, Gaonyalelwe Maribe, Philippe Naveau +1
In recent years several attempts have been made to extend tail modelling towards the modal part of the data. Frigessi et al. (2002) introduced dynamic mixtures of two components wi…
Penalized bias reduction in extreme value estimation for censored Pareto-type data, and long-tailed insurance applications
Jan Beirlant, Gaonyalelwe Maribe, Andrehette Verster
The subject of tail estimation for randomly censored data from a heavy tailed distribution receives growing attention, motivated by applications for instance in actuarial statistic…