Let (X1, Y1), (X2, Y2),…, (Xn, Yn) be a random sample from a bivariate distribution function F which is in the domain of attraction of a bivariate extreme value distribution function G. This G is characterized by the extreme value indices and its spectral measure or angular measure. The extreme value indices determine both the marginals and the spectral measure determines the dependence structure. In this paper, we construct an empirical measure, based on the sample, which is a consistent estimator of the spectral measure. We also show for positive extreme value indices the asymptotic normality of the estimator under a suitable 2nd order strengthening of the bivariate domain of attraction condition
One of the main goal of extreme value analysis is to estimate the probability of rare events given a...
Consider n i.i.d. random elements on C[0; 1].We show that under an appropriate strengthening of the ...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...
Let (X1, Y1), (X2, Y2),…, (Xn, Yn) be a random sample from a bivariate distribution function F which...
AbstractLet (X1, Y1), (X2, Y2),…, (Xn, Yn) be a random sample from a bivariate distribution function...
Let 11 nn be a random sample from a bivariate distri-bution functionF in the domain of max...
Consider a random sample from a bivariate distribution function F in the max-domain of attraction of...
Consider a random sample from a bivariate distribution function F in the max-domain of attraction of...
The traditional approach to multivariate extreme values has been through the multivariate extreme va...
Let F and G be multivariate probability distribution functions, each with equal one dimensional marg...
The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme-val...
AbstractLet F and G be multivariate probability distribution functions, each with equal one dimensio...
The tail behaviour of many bivariate distributions with unit Fréchet margins can be characterised by...
The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme val...
One of the main goal of extreme value analysis is to estimate the probability of rare events given a...
Consider n i.i.d. random elements on C[0; 1].We show that under an appropriate strengthening of the ...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...
Let (X1, Y1), (X2, Y2),…, (Xn, Yn) be a random sample from a bivariate distribution function F which...
AbstractLet (X1, Y1), (X2, Y2),…, (Xn, Yn) be a random sample from a bivariate distribution function...
Let 11 nn be a random sample from a bivariate distri-bution functionF in the domain of max...
Consider a random sample from a bivariate distribution function F in the max-domain of attraction of...
Consider a random sample from a bivariate distribution function F in the max-domain of attraction of...
The traditional approach to multivariate extreme values has been through the multivariate extreme va...
Let F and G be multivariate probability distribution functions, each with equal one dimensional marg...
The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme-val...
AbstractLet F and G be multivariate probability distribution functions, each with equal one dimensio...
The tail behaviour of many bivariate distributions with unit Fréchet margins can be characterised by...
The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme val...
One of the main goal of extreme value analysis is to estimate the probability of rare events given a...
Consider n i.i.d. random elements on C[0; 1].We show that under an appropriate strengthening of the ...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...