Inference for bivariate extremes via a semi-parametric angular-radial model
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912586638819328 |
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| author | Murphy-Barltrop, Callum John Rowlandson Mackay, Ed Jonathan, Philip |
| author_facet | Murphy-Barltrop, Callum John Rowlandson Mackay, Ed Jonathan, Philip |
| contents | The modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for multivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series', with results indicating generally good performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference for bivariate extremes via a semi-parametric angular-radial model Murphy-Barltrop, Callum John Rowlandson Mackay, Ed Jonathan, Philip Methodology Applications The modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for multivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series', with results indicating generally good performance. |
| title | Inference for bivariate extremes via a semi-parametric angular-radial model |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2401.07259 |