9 citations · 17 across the 14 of their papers we have counts for
19 papers
Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares
V. Nesrstová, I. Wilms, J. Palarea-Albaladejo +4
High-dimensional compositional data are commonplace in the modern omics sciences amongst others. Analysis of compositional data requires a proper choice of orthonormal coordinate r…
Robust and Sparse Multinomial Regression in High Dimensions
Fatma Sevinç Kurnaz, Peter Filzmoser
A robust and sparse estimator for multinomial regression is proposed for high dimensional data. Robustness of the estimator is achieved by trimming the observations, and sparsity o…
Identifying the root cause of cable network problems with machine learning
Georg Heiler, Thassilo Gadermaier, Thomas Haider +2
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming.…
Extending compositional data analysis from a graph signal processing perspective
Christopher Rieser, Peter Filzmoser
Traditional methods for the analysis of compositional data consider the log-ratios between all different pairs of variables with equal weight, typically in the form of aggregated c…
Compositional Cubes: A New Concept for Multi-factorial Compositions
Kamila Fačevicová, Peter Filzmoser, Karel Hron
Compositional data are commonly known as multivariate observations carrying relative information. Even though the case of vector or even two-factorial compositional data (compositi…
The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data
Georg Heiler, Allan Hanbury, Peter Filzmoser
Evaluating relative changes leads to additional insights which would remain hidden when only evaluating absolute changes. We analyze a dataset describing mobility of mobile phones…