Bayesian Uncertainty Management in Temporal Dependence of Extremes

TL;DR

This paper introduces a Bayesian semiparametric method to better model and infer the clustering of extremes in stationary time series, addressing limitations of existing approaches and improving estimation accuracy.

Contribution

It develops a Bayesian approach for extremal dependence modeling that overcomes inefficiencies and uncertainty assessment issues in previous methods.

Findings

Improved estimation of extremal dependence properties.

Effective modeling of both short- and long-range dependence.

Enhanced uncertainty quantification in extremal analysis.

Abstract

Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long- and short-range dependence of extremes may both appear. In applications, an assumption of long-range independence often seems reasonable, but short-range dependence, i.e., the clustering of extremes, needs attention. The extremal index $0<\theta\le 1$ is a natural limiting measure of clustering, but for wide classes of dependent processes, including all stationary Gaussian processes, it cannot distinguish dependent processes from independent processes with $\theta=1$. Eastoe and Tawn (2012) exploit methods from multivariate extremes to treat the subasymptotic extremal dependence structure of stationary time series, covering both $0<\theta<1$ and $\theta=1$, through the introduction of a threshold-based extremal index. Inference for their dependence models uses an inefficient stepwise procedure that has various weaknesses and has no reliable assessment of uncertainty. We overcome these issues using a Bayesian semiparametric approach. Simulations and the analysis of a UK daily river flow time series show that the new approach provides improved efficiency for estimating properties of functionals of clusters.