3 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…
cs.CL2024
Rule by Rule: Learning with Confidence through Vocabulary Expansion
Albert Nössig, Tobias Hell, Georg Moser
In this paper, we present an innovative iterative approach to rule learning specifically designed for (but not limited to) text-based data. Our method focuses on progressively expa…