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Data-driven inference for stochastic models

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Christensen, S., Strauch, C. & Trottner, L. (2024). Learning to reflect: A unifying approach for data-driven stochastic control strategies. Bernoulli, 30(3), 2074-2101. https://doi.org/10.3150/23-BEJ1665

Dexheimer, N. & Strauch, C. (2024). On Lasso and Slope drift estimators for Lévy-driven Ornstein-Uhlenbeck processes. Bernoulli, 30(1), 88-116. https://doi.org/10.3150/22-BEJ1574

Trottner, L., Aeckerle-Willems, C. & Strauch, C. (2023). Concentration analysis of multivariate elliptic diffusions. Journal of Machine Learning Research, 24(106), 1-38. 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

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

Revised 24.10.2024

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Lars Madsen