Asymptotic posterior normality of the generalized extreme value distribution
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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.
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