Software & Publications

Software:

Peer reviewed articles:

  • 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.
  • C. Muehlmann, K. Nordhausen, and M. Yi. On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction. IEEE Geoscience and Remote Sensing Letters, volume 18(11), pages 1931-1935, 2021. doi: 10.1109/LGRS.2020.3011549.
  • 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.

Submitted articles:

  • C. Muehlmann, F. Bachoc, and K. Nordhausen. Blind source separation for non-stationary random fields. Submitted. Preprint available at arXiv:2107.01916, 2021.
  • C. Muehlmann, P. Filzmoser, and K. Nordhausen. Spatial Blind Source Separation in the Presence of a Drift. Submitted. Preprint available at arXiv:2108.13813, 2021.
  • N. Piccolotto, M. Bögl, T. Gschwandtner, C. Muehlmann, K. Nordhausen, P. Filzmoser and S. Miksch. TBSSvis: Visual Analytics for Temporal Blind Source Separation. Submitted. Preprint available at arXiv:2011.09896, 2020.
  • C. Muehlmann, F. Bachoc, K. Nordhausen, and M. Yi. Test of the Latent Dimension of a Spatial Blind Source Separation Model. Submitted. Preprint available at arXiv:2011.01711, 2020.

Theses:

  • 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.