In the classical setting of bivariate extreme value theory, the procedures for estimating the probability of an extreme event are not applicable if the componentwise maxima of the observations are asymptotically independent. To cope with this problem, Ledford and Tawn proposed a submodel in which the penultimate dependence is characterized by an additional parameter. We discuss the asymptotic properties of two estimators for this parameter in an extended model. Moreover, we develop an estimator for the probability of an extreme event that works in the case of asymptotic independence as well as in the case of asymptotic dependence, and prove its consistency
Inference over multivariate tails often requires a number of assumptions which may affect the assess...
Multivariate extremes behave very differently under asymptotic dependence as compared to asymptotic ...
Probabilistic and statistical aspects of extremes of univariate processes have been extensively stud...
In the classical setting of bivariate extreme value theory, the procedures for estimating the probab...
In the classical setting of bivariate extreme value theory, the procedures to estimate the probabili...
A fundamental issue in applied multivariate extreme value (MEV) analysis is modelling dependence wit...
In this paper we shall give an alternative derivation of the coefficient of tail dependence introduc...
A number of different dependence scenarios can arise in the theory of multivariate extremes, entaili...
In the bivariate setting, i.e. when we have two variables jointly playing a role, the estimation of ...
Bivariate extreme value distributions arise as the limiting distributions of renormalized componentw...
The Ledford and Tawn model for the bivariate tail incorporates a coefficient, $\eta$, as a measure o...
Bivariate extreme value distributions arise as the limiting distributions of renormalized componentw...
Many multivariate analyses require the account of extreme events. Correlation is an insufficient me...
We propose a multivariate extreme value threshold model for joint tail estimation which overcomes th...
The traditional approach to multivariate extreme values has been through the multivariate extreme va...
Inference over multivariate tails often requires a number of assumptions which may affect the assess...
Multivariate extremes behave very differently under asymptotic dependence as compared to asymptotic ...
Probabilistic and statistical aspects of extremes of univariate processes have been extensively stud...
In the classical setting of bivariate extreme value theory, the procedures for estimating the probab...
In the classical setting of bivariate extreme value theory, the procedures to estimate the probabili...
A fundamental issue in applied multivariate extreme value (MEV) analysis is modelling dependence wit...
In this paper we shall give an alternative derivation of the coefficient of tail dependence introduc...
A number of different dependence scenarios can arise in the theory of multivariate extremes, entaili...
In the bivariate setting, i.e. when we have two variables jointly playing a role, the estimation of ...
Bivariate extreme value distributions arise as the limiting distributions of renormalized componentw...
The Ledford and Tawn model for the bivariate tail incorporates a coefficient, $\eta$, as a measure o...
Bivariate extreme value distributions arise as the limiting distributions of renormalized componentw...
Many multivariate analyses require the account of extreme events. Correlation is an insufficient me...
We propose a multivariate extreme value threshold model for joint tail estimation which overcomes th...
The traditional approach to multivariate extreme values has been through the multivariate extreme va...
Inference over multivariate tails often requires a number of assumptions which may affect the assess...
Multivariate extremes behave very differently under asymptotic dependence as compared to asymptotic ...
Probabilistic and statistical aspects of extremes of univariate processes have been extensively stud...