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Data-driven inference for stochastic models

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Trottner, L., Strauch, C. & Aeckerle-Willems, C. (2023). Concentration analysis of multivariate elliptic diffusions. Journal of Machine Learning Research, 24, 1--38. [106]. https://www.jmlr.org/papers/volume24/22-0666/22-0666.pdf

Dexheimer, N., Strauch, C. & Trottner, L. (2022). Adaptive invariant density estimation for continuous-time mixing Markov processes under sup-norm risk. Annales de l'institut Henri Poincare (B) Probability and Statistics, 58(4), 2029-2064. https://doi.org/10.1214/21-AIHP1235

Dexheimer, N. & Strauch, C. (2022). Estimating the characteristics of stochastic damping Hamiltonian systems from continuous observations. Stochastic Processes and Their Applications, 153, 321-362. https://doi.org/10.1016/j.spa.2022.08.008

Dexheimer, N. & Strauch, C. (Accepted/In press). On Lasso and Slope drift estimators for Lévy-driven Ornstein-Uhlenbeck processes. Bernoulli. https://arxiv.org/abs/2205.07813

Christensen, S., Strauch, C. & Trottner, L. (2021). Learning to reflect: A unifying approach for data-driven stochastic control strategies. https://arxiv.org/abs/2104.11496

Christensen, S. & Strauch, C. (2019). Nonparametric learning for impulse control problems. https://arxiv.org/abs/1909.09528

Revised 18.08.2022

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Lars Madsen