A Bayesian analysis of the Gumbel distribution: an application to extreme rainfall data

Abstract

With the objective of modelling annual rainfall maximum intensities in different geographical zones of Chile, we have created a Bayesian inference method for the generalized extreme value type I distribution (Gumbel distribution). We considered an uninformative prior distribution for the location parameter, μ, and three different prior distributions for the scale parameter, σ. Under these conditions we obtained the posterior distribution of (μ, σ) and associated summary statistics such as modes, expected values, quantiles and credibility intervals. In order to predict and estimate return periods, we obtained the posterior distribution of future observations, its expected value, quantiles and credibility intervals. To obtain several of these posterior summary measures it was necessary to utilize both numerical and Laplace approximations. Furthermore we estimate return period curves and intensity–duration–frequency curves.