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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 assu ...
Extreme events are responsible for huge material damage and are costly in terms of their human and economic impacts. They strike all facets of modern society, such as physical infrastructure and insurance companies through environmental hazards, banking an ...
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 assum ...
Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then th ...
Frequentist and Bayesian approaches to statistics have long been seen as incompatible, but recent work has been done to try and unify them (Bayarri and Berger, 2004; Efron, 2005). Empirical Bayes, approximate Bayesian analysis, and the matching prior appro ...
Heavy-tail phenomena are common in real-life data; the finance and insurance industries, telecommunications, and environment-related events offer typical examples of such phenomena. We focus on the particular topic of the extremogram, for which Davis and M ...
Linear regression summarises the link between a variable of interest and one or several explanatory variables through a parametric relationship. Hastie and Tibshirani (1986) extended this approach by allowing a non-parametric description of the dependence ...
Extreme events can be statistically characterised as excesses of a high threshold. Inference in this case has to account for dependence between excesses. The peaks over threshold approach suggests pre-processing the series by defining clusters of successiv ...