2 papers
stat.AP2026
The data-driven extreme value distribution: non-parametric tail estimation with a derived stability criterion
Michael Sandbichler, Tobias Hell
Quantifying the likelihood of extreme events underpins risk assessment, yet classical Extreme Value Theory relies on asymptotic assumptions that fail in the data-sparse, non-statio…
math.ST2026
Data driven extreme value distribution estimation: Derivation of the Mean Integrated Squared Error, optimal bandwidth selection and stability conditions
Michael Sandbichler, Tobias Hell
We introduce the data driven extreme value distribution (DDEVD) estimator, a kernel-based method for estimating extreme value distributions from data. We derive its mean integrated…