A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities
In this paper, we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible for posterior density estimation and posterior inference for hig...
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| Veröffentlicht in: | Journal of applied statistics 2015-02, Vol.42 (2), p.267-280 |
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| container_title | Journal of applied statistics |
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| creator | Fuquene Patino, Jairo Alberto |
| description | In this paper, we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible for posterior density estimation and posterior inference for high quantiles. The model works well even for small sample sizes and in the absence of prior information. We evaluate the performance of the proposed model through a simulation study. Finally, the proposed model is applied to a real environmental data. |
| doi_str_mv | 10.1080/02664763.2014.947357 |
| format | Article |
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| identifier | ISSN: 0266-4763 |
| ispartof | Journal of applied statistics, 2015-02, Vol.42 (2), p.267-280 |
| issn | 0266-4763 1360-0532 |
| language | eng |
| recordid | cdi_proquest_journals_1625907902 |
| source | Business Source Complete; Taylor & Francis:Master (3349 titles) |
| subjects | Applied statistics Bayesian analysis Computer simulation Density Dirichlet problem Dirichlet process mixture Estimating techniques Extreme values generalized Pareto distribution Parameter estimation Pareto optimality Pareto optimum Quantiles Samples Simulation Studies threshold estimation |
| title | A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities |
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