4.4 Article

Regression models to dependence for exceedance

Journal

JOURNAL OF APPLIED STATISTICS
Volume 48, Issue 16, Pages 3048-3059

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2020.1795088

Keywords

Bayesian; MCMC; extremes; GPD distribution; river dependence

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Extreme Value Theory (EVT) focuses on studying the tails of probability distributions to measure extreme events, in river flow data, extreme levels may be related to neighboring rivers, a Bayesian model was proposed to describe the dependence between rivers using a conditional independent structure.
Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.

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