  Bias reduction in tail estimation has received considerable interest in
extreme value analysis. Estimation methods that minimize the bias while keeping
the mean squared error (MSE) under control, are especially useful when applying
classical methods such as the Hill (1975) estimator. In Caeiro et al. (2005)
minimum variance reduced bias estimators of the Pareto tail index were first
proposed where the bias is reduced without increasing the variance with respect
to the Hill estimator. This method is based on adequate external estimation of
a pair of second-order parameters. Here we revisit this problem from a Bayesian
point of view starting from the extended Pareto distribution (EPD)
approximation to excesses over a high threshold, as developed in Beirlant et
al. (2009) using maximum likelihood (ML) estimation. Using asymptotic
considerations, we derive an appropriate choice of priors leading to a Bayes
estimator for which the MSE curve is a weighted average of the Hill and EPD-ML
MSE curves for a large range of thresholds, under the same conditions as in
Beirlant et al.(2009). A similar result is obtained for tail probability
estimation. Simulations show surprisingly good MSE performance with respect to
the existing estimators.
