Abstract
Although extreme events can occur rarely, they may have significant social and economic impacts. To assess the risk of extreme events, it is important to study the extreme quantiles of the distribution. The accurate semi-parametric estimation of high quantiles depends strongly on the estimation of some crucial parameters that appear in extreme value theory. Procedures that combine extreme value theory and time series modelling have revealed themselves as a nice compromise to capture extreme events. Here we study the estimation of extreme quantiles after adequate time series modelling. Using the R software, our approach will be applied to the daily mean flow discharge rate values of two rivers in Portugal.
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Notes
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From [18].
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Acknowledgements
Manuela Neves and Clara Cordeiro are partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. Dora Prata Gomes is financed by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (Center for Mathematics and Applications).
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Cordeiro, C., Gomes, D.P., Neves, M.M. (2023). Time Series Procedures to Improve Extreme Quantile Estimation. In: Kitsos, C.P., Oliveira, T.A., Pierri, F., Restaino, M. (eds) Statistical Modelling and Risk Analysis. ICRA 2022. Springer Proceedings in Mathematics & Statistics, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-031-39864-3_6
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