Inference for bivariate extremes via a semi-parametric angular-radial model

TL;DR

This paper introduces a semi-parametric angular-radial model for multivariate extreme value analysis, offering a flexible and unified approach to assess joint tail behavior in various applications.

Contribution

The paper presents a novel semi-parametric inference framework for multivariate extremes that overcomes limitations of existing methods and includes tools for uncertainty and goodness-of-fit assessment.

Findings

Good performance on simulated data

Effective analysis of observed metocean time series

Unified approach for joint tail behavior

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 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.