# Asymptotic posterior normality of the generalized extreme value distribution

The univariate generalized extreme value (GEV) distribution is the most commonly used tool for analysing the properties of rare events. The ever greater utilization of Bayesian methods for extreme value analysis warrants detailed theoretical investigation, which has thus far been underdeveloped. Even the most basic asymptotic results are difficult to obtain because the GEV fails to satisfy standard regularity conditions. Here, we prove that the posterior distribution of the GEV parameter vector, given an independent and identically distributed sequence of observations, converges to a normal distribution centred at the true parameter. The proof necessitates analysing integrals of the GEV likelihood function over the entire parameter space, which requires considerable care because the support of the GEV density depends on the parameters in complicated ways.

## Authors

• 4 publications
• 6 publications
08/14/2020

### Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution

The three-parameter generalized extreme value distribution arises from c...
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### A note on power generalized extreme value distribution and its properties

Similar to the generalized extreme value (GEV) family, the generalized e...
07/23/2020

### The r-largest four parameter kappa distribution

The generalized extreme value distribution (GEVD) has been widely used t...
07/22/2019

### Asymptotic normality, concentration, and coverage of generalized posteriors

Generalized likelihoods are commonly used to obtain consistent estimator...
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### A Bimodal Model for Extremes Data

In extreme values theory, for a sufficiently large block size, the maxim...
02/17/2021

### Deep Extreme Value Copulas for Estimation and Sampling

We propose a new method for modeling the distribution function of high d...
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