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.
2021
Empirical Bayes inference for the Peaks Over a Threshold method
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.
Extreme Values
Monte Carlo methods
Censored likelihood
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/35393