3 papers
math.ST2026
Properties of stepwise parameter estimation in high-dimensional vine copulas
Jana Gauss, Thomas Nagler
The increasing use of vine copulas in high-dimensional settings, where the number of parameters is often of the same order as the sample size, calls for asymptotic theory beyond th…
stat.ML2025
DCSI -- An improved measure of cluster separability based on separation and connectedness
Jana Gauss, Fabian Scheipl, Moritz Herrmann
Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be q…
math.ST2025
Asymptotics for estimating a diverging number of parameters -- with and without sparsity
Jana Gauss, Thomas Nagler
We develop a general asymptotic theory for estimating equations whose dimension diverges with the sample size. For both unpenalized and sparse penalized problems, we establish popu…