Contributions to extreme value theory : Trend detection for heteroscedastic extremes

Abstract : We firstly present in this thesis the permutation Bootstrap method applied for the block maxima (BM) method in extreme value theory. The method is based on BM ranks whose distribution is presented and simulated. It performs well and leads to a variance reduction in the estimation of the GEV parameters and the extreme quantiles. Secondly, we build upon the heteroscedastic extremes framework by Einmahl et al. (2016) where the observations are assumed independent but not identically distributed and the variation in their tail distributions is modeled by the so-called skedasis function. While the original paper focuses on non-parametric estimation of the skedasis function, we consider here parametric models and prove the consistency and asymptotic normality of the parameter estimators. A parametric test for trend detection in the case where the skedasis function is monotone is introduced. A short simulation study shows that the parametric test can be more powerful than the non-parametric Kolmogorov-Smirnov type test, even for misspecified models. We also discuss the choice of threshold based on Lepski's method. The methodology is finally illustrated on a dataset of minimal/maximal daily temperatures in Fort Collins, Colorado, during the 20th century. Thirdly, we have a training sample data of daily maxima precipitation over 24 years in 40 stations. We make spatio-temporal prediction of quantile of level corresponding to extreme monthly precipitation over the next 20 years in every station. We use generalized extreme value models by incorporating covariates. After selecting the best model based on the Akaike information criterion and the k-fold cross validation method, we present the results of the estimated quantiles for the selected models. Finally, we study the wind speed and wave height risks in Beddawi region in the northern Lebanon during the winter season in order to protect the oil rig that will be installed. We estimate the return levels associated to return periods of 50, 100 and 500 years for each risk separately using the univariate extreme value theory. Then, by using the multivariate extreme value theory we estimate the dependence between extreme wind speed and wave height as well as joint exceedance probabilities and joint return levels to take into consideration the risk of these two environmental factors simultaneously.
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Aline Mefleh. Contributions to extreme value theory : Trend detection for heteroscedastic extremes. Statistics Theory [stat.TH]. Université Bourgogne Franche-Comté, 2018. English. ⟨NNT : 2018UBFCD032⟩. ⟨tel-01881323⟩

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