Bayesian uncertainty management in temporal dependence of extremes

Lugrin, T. and Davison, A. C. and Tawn, J. A. (2016) Bayesian uncertainty management in temporal dependence of extremes. Extremes, 19 (3). pp. 491-515. ISSN 1572-915X

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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 < ≤ 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 = 1. Eastoe and Tawn (Biometrika 99, 43–55 2012) exploit methods from multivariate extremes to treat the subasymptotic extremal dependence structure of stationary time series, covering both 0 < <1 and = 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.

Item Type:
Journal Article
Journal or Publication Title:
Extremes
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-016-0258-0
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
83164
Deposited By:
Deposited On:
28 Nov 2016 09:40
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Oct 2020 05:18