6 citations · 6 across the 6 of their papers we have counts for
6 papers
Sparse Estimation for High-Dimensional Lévy-driven Ornstein--Uhlenbeck Processes from Discrete Observations
Niklas Dexheimer, Natalia Jeszka
We study high-dimensional drift estimation for Lévy-driven Ornstein--Uhlenbeck processes based on discrete observations. Assuming sparsity of the drift matrix, we analyze Lasso and…
Data-driven optimal stopping: A pure exploration analysis
Sören Christensen, Niklas Dexheimer, Claudia Strauch
The standard theory of optimal stopping is based on the idealised assumption that the underlying process is essentially known. In this paper, we drop this restriction and study dat…
Adaptive nonparametric drift estimation for multivariate jump diffusions under sup-norm risk
Niklas Dexheimer
We investigate nonparametric drift estimation for multidimensional jump diffusions based on continuous observations. The results are derived under anisotropic smoothness assumption…
On Lasso and Slope drift estimators for Lévy-driven Ornstein--Uhlenbeck processes
Niklas Dexheimer, Claudia Strauch
We investigate the problem of estimating the drift parameter of a high-dimensional Lévy-driven Ornstein--Uhlenbeck process under sparsity constraints. It is shown that both Lasso a…
Estimating the characteristics of stochastic damping Hamiltonian systems from continuous observations
Niklas Dexheimer, Claudia Strauch
We consider nonparametric invariant density and drift estimation for a class of multidimensional degenerate resp. hypoelliptic diffusion processes, so-called stochastic damping Ham…
Mixing it up: A general framework for Markovian statistics
Niklas Dexheimer, Claudia Strauch, Lukas Trottner
Up to now, the nonparametric analysis of multidimensional continuous-time Markov processes has focussed strongly on specific model choices, mostly related to symmetry of the semigr…