Abstract
The article takes up Bayesian inference in extreme value distributions and also considers extreme value regression, which appears relatively uncommon in the regression literature. Numerical methods are organized around Gibbs sampling. It is shown that simple and reliable numerical techniques can be devised by exploiting the particular form of the posterior conditional distributions. The sampling behaviour of the proposed estimators is also explored via Monte Carlo simulation.
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