Software & Publications

Software:

• C. Muehlmann, N. Piccolotto, S. De Iaco and K. Nordhausen. SpaceTimeBSS: Blind Source Separation for Multivariate Spatio-Temporal Data, R package, 2022. https://CRAN.R-project.org/package=SpaceTimeBSS

• C. Muehlmann, K. Nordhausen, and J. Virta. SpatialBSS: Blind Source Separation for Multivariate Spatial Data, R package, 2021. https://CRAN.R-project.org/package=SpatialBSS

Peer reviewed articles:

• Muehlmann, C., De Iaco, S. and Nordhausen, K. (2022): Blind Recovery of Sources for Multivariate Space-Time Data. Accepted for publication in Stochastic Environmental Research and Risk Assessment.

• Muehlmann, C., Bachoc, F., Nordhausen, K. and Yi, M. (2022): Test of the Latent Dimension of a Spatial Blind Source Separation Model. Accepted for publication in Statistica Sinica.

• Piccolotto, N., Bögl, M., Gschwandtner, T. Muehlmann, C., Nordhausen, K., Filzmoser, P. and Miksch, S. (2022): TBSSvis: Visual Analytics for Temporal Blind Source Separation. Accepted for publication in Visual Informatics.

• Babek, O., Facevicova, K., Zidek, M., Sedlacek, J., Muehlmann, C., Nordhausen, K. and Hron, K. (2022): X-Ray Fluorescence Scanning of Soft and Wet-Sediment Cores for Environmental Risk Assessment; A Robust Blind Source Separation Approach. Journal of Geochemical Exploration, 243, 107106.

• Piccolotto, N., Bögl, M., Mühlmann, C., Nordhausen, K., Filzmoser, P. and Miksch, S. (2022): Visual Parameter Selection for Spatial Blind Source Separation. Computer Graphics Forum, 41, 157-168.

• Muehlmann, C., Bachoc, F. and Nordhausen, K. (2022): Blind Source Separation for Non-Stationary Random Fields. Spatial Statistics, 47, 100574.

• 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.

• 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, P. Filzmoser, and K. Nordhausen (2022): Spatial Blind Source Separation in the Presence of a Drift.

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.