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 bivariate 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.
%0 Journal Article
%1 Murphy-Barltrop2024
%A Murphy-Barltrop, Callum John Rowlandson
%A Mackay, Ed
%A Jonathan, Philip
%D 2024
%J Extremes
%K imported
%R 10.1007/s10687-024-00492-2
%T Inference for Bivariate Extremes via a Semi-Parametric Angular-Radial Model
%U https://doi.org/10.1007/s10687-024-00492-2
%X 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 bivariate 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.
@article{Murphy-Barltrop2024,
abstract = {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 bivariate 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.},
added-at = {2024-10-17T10:37:28.000+0200},
author = {Murphy-Barltrop, Callum John Rowlandson and Mackay, Ed and Jonathan, Philip},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2b2e0ab8e2fb2e9bacd566a5833495fdc/scadsfct},
day = 03,
doi = {10.1007/s10687-024-00492-2},
interhash = {6753aca210efbb497097dc2b4bed33d7},
intrahash = {b2e0ab8e2fb2e9bacd566a5833495fdc},
issn = {1572-915X},
journal = {Extremes},
keywords = {imported},
month = oct,
timestamp = {2024-10-17T10:40:52.000+0200},
title = {Inference for Bivariate Extremes via a Semi-Parametric Angular-Radial Model},
url = {https://doi.org/10.1007/s10687-024-00492-2},
year = 2024
}
