Empirical Bayes inference for the block maxima method

TL;DR

This paper introduces an empirical Bayes approach for the block maxima method in extreme value analysis, providing theoretical guarantees and efficient computation, demonstrated through simulations and hurricane wind data analysis.

Contribution

It develops a novel empirical Bayes inference framework for the block maxima method, with proven theoretical properties and practical computational algorithms.

Findings

Posterior distributions satisfy key theoretical properties.

Method performs well with modest sample sizes.

Effective in analyzing hurricane wind extremes.

Abstract

The block maxima method is one of the most popular approaches for extreme value analysis with independent and identically distributed observations in the domain of attraction of an extreme value distribution. The lack of a rigorous study on the Bayesian inference in this context has limited its use for statistical analysis of extremes. In this paper we propose an empirical Bayes procedure for inference on the block maxima law and its related quantities. We show that the posterior distributions of the tail index of the data distribution and of the return levels (representative of future extreme episodes) satisfy a number of important theoretical properties. These guarantee the reliability of posterior-based inference and extend to the posterior predictive distribution, the key tool in Bayesian probabilistic forecasting. Posterior computations are readily obtained via an efficient adaptive Metropolis-Hasting type of algorithm. Simulations show its excellent inferential performances already with modest sample sizes. The utility of our proposal is showcased analysing extreme winds generated by hurricanes in the Atlantic basin.