13 citations · 40 across the 9 of their papers we have counts for
10 papers
Data Type Agnostic Visual Sensitivity Analysis
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +4
Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial da…
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
François Bachoc, Christoph Muehlmann, Klaus Nordhausen +1
Non-stationary source separation is a well-established branch of blind source separation with many different methods. However, for none of these methods large-sample results are av…
Visual Parameter Selection for Spatial Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +3
Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to m…
Spatial Blind Source Separation in the Presence of a Drift
Christoph Muehlmann, Peter Filzmoser, Klaus Nordhausen
Multivariate measurements taken at different spatial locations occur frequently in practice. Proper analysis of such data needs to consider not only dependencies on-sight but also…
Blind source separation for non-stationary random fields
Christoph Muehlmann, François Bachoc, Klaus Nordhausen
Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely…
TBSSvis: Visual Analytics for Temporal Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Theresia Gschwandtner +4
Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to…
Test of the Latent Dimension of a Spatial Blind Source Separation Model
Christoph Muehlmann, François Bachoc, Klaus Nordhausen +1
We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated Gaussian random fields contai…
Sliced Inverse Regression for Spatial Data
Christoph Muehlmann, Hannu Oja, Klaus Nordhausen
Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recent…
On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction
Christoph Muehlmann, Klaus Nordhausen, Mengxi Yi
Multivariate measurements taken at irregularly sampled locations are a common form of data, for example in geochemical analysis of soil. In practical considerations predictions of…
Independent Component Analysis for Compositional Data
Christoph Muehlmann, Kamila Fačevicová, Alžběta Gardlo +2
Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of si…