Detecting distributional changes in samples of independent block maxima using probability weighted moments

TL;DR

This paper develops and evaluates statistical tests for detecting distributional changes in independent block maxima, with applications in environmental sciences like hydrology and climatology.

Contribution

It introduces tests for non-GEV distributed maxima, analyzes their asymptotic null distributions, and demonstrates their effectiveness through simulations and real data.

Findings

Tests have good finite-sample performance.

Effective in detecting distributional changes.

Applicable to environmental datasets.

Abstract

The analysis of seasonal or annual block maxima is of interest in fields such as hydrology, climatology or meteorology. In connection with the celebrated method of block maxima, we study several tests that can be used to assess whether the available series of maxima is identically distributed. It is assumed that block maxima are independent but not necessarily generalized extreme value distributed. The asymptotic null distributions of the test statistics are investigated and the practical computation of approximate p-values is addressed. Extensive Monte-Carlo simulations show the adequate finite-sample behavior of the studied tests for a large number of realistic data generating scenarios. Illustrations on several environmental datasets conclude the work.