Simulating Multivariate Extreme Value Distributions of Logistic Type

94 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Statistics of Extremes (2004, methodology): “3, we give an overview of possible kinds of GRV 2 functions and the corresponding representations for U and log U as given in Vanroelen (2003). From this list, it follows that the second-order rate in (5.”
Models for Dependent Extremes Using Stable Mixtures (2007, methodology): “According to it, one can first simulate the stable variables, using the method of Chambers et al. (1976), and then simulate independent variables from the conditional distribution given the stable variables, cf. Stephenson (2003).”
Nonparametric estimation of multivariate extreme-value copulas (2011, methodology): “Simulations were performed using the R-packagevd (Stephenson, 2002), which implements the algorithms presented in Stephenson (2003).”
Nonparametric estimation of the dependence function for a multivariate extreme value distribution (2008, methodology): “Simulation study To evaluate the performance of different estimators for the Pickands dependence function, we used the algorithms in Stephenson [15] to simulate trivariate extreme values whose dependence functions are logistic-type [17].”
Bayesian composite marginal likelihoods (2011, methodology): “We employ the routines included in the R package evd (Stephenson (2003)) to simulate from the logistic distribution and to compute the full likelihood L(α) and the bivariate densities needed for the calculation of PL(α) and PL c (α).”