Comment on Pisarenko et al. "Characterization of the Tail of the Distribution of Earthquake Magnitudes by Combining the GEV and GPD Descriptions of Extreme Value Theory"
Mathias Raschke

TL;DR
This paper critiques the application of extreme value theory to earthquake magnitudes, emphasizing the limitations of GEVD and GPD models for truncated exponential distributions and proposing alternative inference methods.
Contribution
It clarifies the asymptotic nature of GEVD and GPD approximations, highlights their limitations for earthquake data, and suggests improved statistical inference approaches.
Findings
Classical EVT models do not perform well for truncated exponential earthquake data.
GPD and GEVD are suitable for different earthquake process types, not only homogeneous Poisson processes.
Poor convergence of earthquake magnitude tails to GPD affects model applicability.
Abstract
In this short note, I comment on the research of Pisarenko et al. (2014) regarding the extreme value theory and statistics in case of earthquake magnitudes. The link between the generalized extreme value distribution (GEVD) as an asymptotic model for the block maxima of a random variable and the generalized Pareto distribution (GPD) as a model for the peak over thresholds (POT) of the same random variable is presented more clearly. Pisarenko et al. (2014) have inappropriately neglected that the approximations by GEVD and GPD work only asymptotically in most cases. This applies particularly for the truncated exponential distribution (TED), being a popular distribution model for earthquake magnitudes. I explain why the classical models and methods of the extreme value theory and statistics do not work well for truncated exponential distributions. As a consequence, the classical methods…
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