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Home List of Titles Accounting for uncertainty in extremal dependence modeling using Bayesian model averaging techniques
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/155358
- Accounting for uncertainty in extremal dependence modeling using Bayesian model averaging techniques
- Apputhurai, P.; Stephenson, A. G.
- Modeling the joint tail of an unknown multivariate distribution can be characterized as modeling the tail of each marginal distribution and modeling the dependence structure between the margins. Classical methods for modeling multivariate extremes are based on the class of multivariate extreme value distributions. However, such distributions do not allow for the possibility of dependence at finite levels that vanishes in the limit. Alternative models have been developed that account for this asymptotic independence, but inferential statistical procedures seeking to combine the classes of asymptotically dependent and asymptotically independent models have been of limited use. We overcome these difficulties by employing Bayesian model averaging to account for both types of asymptotic behavior, and for subclasses within the asymptotically independent framework. Our approach also allows for the calculation of posterior probabilities of different classes of models, allowing for direct comparison between them. We demonstrate the use of joint tail models based on our broader methodology using two oceanographic datasets and a brief simulation study.
- Publication type
- Journal article
- Research centre
- Swinburne University of Technology. Faculty of Life and Social Sciences
- Journal of Statistical Planning and Inference, Vol. 141, no. 5 (May 2011), pp. 1800-1807
- Publication year
- FOR Code(s)
- 0104 Statistics
- Bayesian model averaging; Multivariate extreme value distribution; Posterior distribution; Prior distribution; Simulation
- Elsevier B.V.
- Publisher URL
- Copyright © 2010 Elsevier B.V. All rights reserved.
- Peer reviewed