Software & Publications


Peer reviewed articles:

  • C. Muehlmann, K. Facevicova, A. Gardlo, H. Janeckova and K. Nordhausen. Independent Component Analysis for Compositional Data. In A. Daouia and A. Ruiz-Gazen, editors, Advances in Contemporary Statistics and Econometrics: Festschrift in Honor of Christine Thomas-Agnan, pages 525–545. Springer, Cham, 2021. doi: 10.1007/978-3-030-73249-3_27.
  • C. Muehlmann, H. Oja, and K. Nordhausen. Sliced Inverse Regression for Spatial Data. In E. Bura and B. Li, editors, Festschrift in Honor of R. Dennis Cook: Fifty Years of Contribution to Statistical Science, pages 87–107. Springer, Cham, 2021. doi: 10.1007/978-3-030-69009-0_5.
  • C. Muehlmann, P. Filzmoser, and K. Nordhausen. Local Difference Matrices for Spatial Blind Source Separation. To appear in Proceedings of the 3rd Conference of the Arabian Journal of Geosciences, 2020.

Submitted articles:

  • C. Muehlmann, N. Piccolotto, C. Capello, M. Bögl, P. Filzmoser, S. Miksch, and K. Nordhausen (2022): Visual Interactive Parameter Selection for Temporal Blind Source Separation.
  • N. Piccolotto, M. Bögl, C. Muehlmann, K. Nordhausen, P. Filzmoser, J. Schmidt, and S. Miksch (2022): Visual sensitivity analysis beyond multivariate parameters.
  • M. Sipila, C. Muehlmann, K. Nordhausen and S. Taskinen (2022): Robust second-order stationary spatial blind source separation using generalized sign matrices.
  • F. Bachoc, C. Muehlmann, K. Nordhausen and J. Virta (2022): Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals.


  • C. Muehlmann. Advances in blind source separation for spatial data. Doctoral thesis. Institute of Statistics and Mathematical Methods in Economics, TU Wien, 2021.
  • C. Muehlmann. Pulse-Shape Discrimination with Deep Learning in CRESST. Institute of High Energy Physics, Austrian Academy of Sciences, 2019.