Bayesian estimation of bivariate Pickands dependence function

Modification of Pickands’ Dependence Function for the Simulate Ordered Bivariate Extreme Data

Abstract: We study the characteristics of the Pickands’ dependence function for bivariate extreme distribution for minima, BEVM, when considering the stochastics ordering of the two variables. The existing Pickand’s dependence function terminologies and theories are modified to suit the dependence functions of extreme cases. We successful implement and apply these functions to our simulate extr...

A New Estimator for the Pickands Dependence Function

Bayesian estimation in Kibble's bivariate gamma distribution

The authors describe Bayesian estimation for the parameters of the bivariate gamma distribution due to Kibble (1941). The density of this distribution can be written as a mixture, which allows for a sim ple data augmentation scheme. The authors propose a Markov chain Monte Carlo algorithm to facilitate estimation. They show that the resulting chain is geometrically ergodic, and thus a regenerat...

A Bayesian Approach to Bivariate Surface Estimation

This paper outlines a general Bayesian approach to estimating a bivariate regression function in a nonpara-metric manner. It models the function using a bivariate regression spline basis with many terms. Binary indicator variables corresponding to these terms are introduced to explicitly model the uncertainty of whether, or not, the terms provide a signiicant contribution to the regression. The...

Bivariate extension of the Pickands–Balkema–de Haan theorem

We prove a two-dimensional version of the famous Pickands–Balkema–de Haan theorem of extreme value theory. The bivariate random variables are generated using the copula language. This representation of dependence structures allows to derive asymptotic results for bivariate excess distributions.  2003 Elsevier SAS. All rights reserved. Résumé Une version en dimension 2 du célèbre théorème de Pi...