We study an empirical Bayes method for extreme values data exploiting a censored likelihood from the GEV distribution. The censoring process is done following a Peak Over a Threshold (POT) approach of the Extreme Values Analysis. We study theoretically the accuracy of the obtained Bayesian credicle regions via simulation study.
We study an empirical Bayes method for extreme values data exploiting a censored likelihood from the GEV distribution. The censoring process is done following a Peak Over a Threshold (POT) approach of the Extreme Values Analysis. We study theoretically the accuracy of the obtained Bayesian credicle regions via simulation study.
Empirical Bayes inference for the Peaks Over a Threshold method
SCANZI, PIETRO
2021/2022
Abstract
We study an empirical Bayes method for extreme values data exploiting a censored likelihood from the GEV distribution. The censoring process is done following a Peak Over a Threshold (POT) approach of the Extreme Values Analysis. We study theoretically the accuracy of the obtained Bayesian credicle regions via simulation study.| File | Dimensione | Formato | |
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Scanzi_Pietro.pdf
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https://hdl.handle.net/20.500.12608/35393