Modelling exceedances in extreme value theory

Quem: 

Dani Gamerman

Onde: 

Via Zoom

Quando: 

18 de Março de 2021, às 16h

Extreme value theory (EVT) is the branch of Statistics concerned with extremes or tails of a distribution. It has a long list of areas of application, including Finance and Environmental Sciences. One of the main concerns of EVT is to model exceedances, or values beyond a sufficiently high quantile. Nice theoretical results suggest the way forward to approximate exceedance behaviour, but do not define how extreme one needs to be for the approximation to work well. Ad-hoc procedures are commonly used to address this issue but they suffer from the pitfalls inherent to such procedures and do not take into account the uncertainty associated. Thus, resulting inference is likely to be biased and/or to underestimate uncertainty. We propose a procedure that avoids such pitfalls by letting the data drive the decision of when the approximation can be safely applied, while accounting for the uncertainty of this choice. The procedures are extended to: 1) accommodate for the inclusion of external sources of information; 2) the time series context to incorporate temporal dependence; 3) identify the extremal behavior, and; 4) handle multivariate contexts.

*Texto informado pelo autor.

 

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Palestrante: 

Dani Gamerman é Professor Titular Visitante da UFMG desde 2019. Professor Titular (1996 a 2019) e Emérito (2021) da UFRJ. É autor dos livros Monte Carlo Markov Chain: Stochastic Simulation for Bayesian Inference (Chapman & Hall, 2006, 2a. edição) e Statistical Inference: an Integrated Approach, com Helio S. Migon e Francisco Louzada (Chapman & Hall, 2014, 2a. edição), além de livros nacionais. Publicou artigos em vários periódicos de Estatística como Journal of the Royal Statistical Society Series B, Biometrika, Applied Statistics, Statistics & Computing e Journal of Multivariate Analysis. Tem suas atividades de pesquisas em modelos dinâmicos, estatística espacial, análise de sobrevivência, teoria de valores extremos, TRI, simulação estocástica e inferência Bayesiana.