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Dr. Anita Hill-Thieler

Statistik in den Biowissenschaften



Research / Areas of Work

Period search in unevenly sampled time series occuring in astroparticle physics (light curves) using robust methods and also in presence of red noise:

 Image filter based on robust regression to smooth images and detect edges:



  • Thieler, A.M. (2014): Robuste Verfahren zur Periodendetektion in ungleichmäßig beobachteten Lichtkurven, Thesis, TU Dortmund University. Link
  • Thieler, A. M., Fried, R., Rathjens, J. (2013): RobPer: An R package to calculate periodograms for light curves based on robust regression, Technical Report from the Collaborative Research Center SFB 876, TU Dortmund University, Nr. 2 (submitted). Link
  • Thieler, A.M., Backes, M., Fried, R., Rhode, W. (2013), "Periodicity detection in irregularly sampled light curves by robust regression and outlier detection", Statistical Analysis and Data Mining 6 (1), 73–89. Link
  • Raabe, N., Thieler, A.M., Weihs, C., Fried, R., Rautert, C., Biermann, D. (2012), "Modeling Material Heterogeneity by Gaussian Random Fields for the Simulation of Inhomogeneous Mineral Subsoil Machining", Proceedings of SIMUL 2012, 97-102. Link
  • Fried, R., Raabe, N., Thieler, A. (2012), "On the robust analysis of periodic nonstationary time series", Proceedings in Computational Statistics COMPSTAT 2012, 245-257.
  • Thieler, A. M. (2009): Filtermethoden auf Basis robuster Regression zur Glättung von Graustufenbildern, Diploma Thesis, TU Dortmund University.