2 citations · 3 across the 3 of their papers we have counts for
3 papers
math.ST2023★ 2 cited
From dense to sparse design: Optimal rates under the supremum norm for estimating the mean function in functional data analysis
Max Berger, Philipp Hermann, Hajo Holzmann
We derive optimal rates of convergence in the supremum norm for estimating the Hölder-smooth mean function of a stochastic process which is repeatedly and discretely observed with…
math.ST2021★ 1 cited
Bounded support in linear random coefficient models: Identification and variable selection
Philipp Hermann, Hajo Holzmann
We consider linear random coefficient regression models, where the regressors are allowed to have a finite support. First, we investigate identifiability, and show that the means a…
math.ST2020
Support estimation in high-dimensional heteroscedastic mean regression
Philipp Hermann, Hajo Holzmann
A current strand of research in high-dimensional statistics deals with robustifying the available methodology with respect to deviations from the pervasive light-tail assumptions.…