Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization
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References listed on IDEAS
- Janet E. Heffernan & Jonathan A. Tawn, 2004. "A conditional approach for multivariate extreme values (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 497-546.
- Einmahl, J.H.J. & Segers, J.J.J., 2008.
"Maximum Empirical Likelihood Estimation of the Spectral Measure of an Extreme Value Distribution,"
2008-42, Tilburg University, Center for Economic Research.
- Einmahl, J.H.J. & Segers, J.J.J., 2009. "Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution," Other publications TiSEM ffef2e15-c4a8-471f-b730-1, Tilburg University, School of Economics and Management.
- Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.
- Cooley, Daniel & Davis, Richard A. & Naveau, Philippe, 2010. "The pairwise beta distribution: A flexible parametric multivariate model for extremes," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2103-2117, October.
- Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
- M.-O. Boldi & A. C. Davison, 2007. "A mixture model for multivariate extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 217-229.
- Simon Guillotte & François Perron & Johan Segers, 2011. "Non‐parametric Bayesian inference on bivariate extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 377-406, June.
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- repec:eee:jmvana:v:161:y:2017:i:c:p:12-31 is not listed on IDEAS
- repec:eee:stapro:v:128:y:2017:i:c:p:60-66 is not listed on IDEAS
- Sabourin, Anne, 2015. "Semi-parametric modeling of excesses above high multivariate thresholds with censored data," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 126-146.
- repec:bla:jorssb:v:79:y:2017:i:1:p:149-175 is not listed on IDEAS
- Lee, J. & Fan, Y. & Sisson, S.A., 2015. "Bayesian threshold selection for extremal models using measures of surprise," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 84-99.
More about this item
KeywordsMultivariate extremes; Semi parametric Bayesian inference; Mixture models; Reversible-jump algorithm;
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