Prof. Dr. Claudia Strauch

Prof. Dr. Claudia Strauch

Assistant professor in Statistics
University of Mannheim
Mathematical Institute
B 6, 26 – Room B 3.16
68159 Mannheim

Research Interests

  • Statistics for (multidimensional) stochastic processes,
  • nonparametric and high-dimensional statistics,
  • data-driven procedures for stochastic control.

Short CV

  • since 10.2017: Assistant professor (W1), Universität Mannheim
  • 04.2017-09.2017: Visiting professor, Technische Universität Braunschweig
  • 04.2015-03.2017: Postdoctoral fellow in the DFG Research Training Group 1953 „Statistical Modeling of Complex Systems and Processes“, Universität Heidelberg
  • 10.2013-03.2015: Research and teaching assistant at the Institute of Mathematics, Universität Hamburg
  • 06.2013: Dr. rer. nat. in Mathematics, Universität Hamburg
  • 10.2012-09.2013: Research fellow in the DFG Priority Program SPP 1324 „Mathematical Methods for Extracting Quantifiable Information from Complex Systems“, Ruhr-Universität Bochum
  • 10.2009-09.2012: Research and teaching assistant at the Institute of Mathematics, Universität Hamburg

Articles

  • „Sup-norm adaptive estimation for scalar ergodic diffusions“, with C. Aeckerle-Willems, 2018, arXiv: 1808.10660
  • „Concentration of scalar ergodic diffusions and some statistical implications“, with C. Aeckerle, 2018, arXiv: 1807.11331
  • „Adaptive invariant density estimation for ergodic diffusions over anisotropic classes“, Ann. Statist., 46, no. 6B, 3451-3480, 2018, Link
  • „Exact adaptive pointwise drift estimation for multidimensional ergodic diffusions“, Probab. Theory Related Fields, 164, no. 1-2, 361-400,2016, Link
  • „Sharp adaptive drift estimation for ergodic diffusions: the multivariate case“, Stochastic Process. Appl., 125, no. 7, 2562-2602, 2015, Link
  • „Uniform central limit theorems for smoothed multidimensional diffusions“, with A. Rohde, 2010, arXiv: 1010.3604

Teaching

  • Spring 2019: Nonparametric Statistics
  • Winter 2018: Einführung in die mathematische Statistik
  • Spring 2018: Computational Finance
  • Winter 2017: Einführung in die Versicherungs­mathematik