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Activity Number: 506
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #306742
Title: Robust Estimators and Influence Measures of Extremal Dependence
Author(s): Yu-Ling Tsai*+ and Duncan Murdoch and Debbie Dupuis
Companies: University of Western Ontario and University of Western Ontario and HEC Montréal
Address: Department of Statistical and Actuarial Sciences, London, ON, N6A 5B7, Canada
Keywords: asymptotic dependence ; Bayesian robustness ; contamination ; influence measure ; multivariate extremes
Abstract:

We achieve robust Bayesian estimation of bivariate extremal dependence through the use of weighted log-likelihood techniques. The new estimator is easy to implement. It can be used for complicated likelihoods such as those involved in multivariate extreme value problems. In the course of assessing whether a Bayesian estimator is robust, we develop a simple influence measure that can be used as a first step in judging the robustness. The proposed measure is easy to compute and successfully captures both the effect of contamination and Monte Carlo uncertainty. We demonstrate our techniques on simulated data.

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