24 citations · 72 across the 24 of their papers we have counts for
30 papers
Sparse Data-Driven Random Projection in Regression for High-Dimensional Data
Roman Parzer, Peter Filzmoser, Laura Vana-Gür
We examine the linear regression problem in a challenging high-dimensional setting with correlated predictors where the vector of coefficients can vary from sparse to dense. In thi…
Identifying Important Pairwise Logratios in Compositional Data with Sparse Principal Component Analysis
Viktorie Nesrstová, Ines Wilms, Karel Hron +1
Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pair…
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…
Minimum regularized covariance trace estimator and outlier detection for functional data
Jeremy Oguamalam, Una Radojičić, Peter Filzmoser
In this paper, we propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection. The MRCT esti…
Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
Lukas Neubauer, Peter Filzmoser
A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain. Due to different properties of each time series su…
Spatially smoothed robust covariance estimation for local outlier detection
Patricia Puchhammer, Peter Filzmoser
Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real data sets from various application areas. This paper int…
Predictive change point detection for heterogeneous data
Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz +2
A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-…
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…
Multivariate outlier explanations using Shapley values and Mahalanobis distances
Marcus Mayrhofer, Peter Filzmoser
For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions origi…
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…
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…
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…
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…
A Robust Adaptive Modified Maximum Likelihood Estimator for the Linear Regression Model
Sukru Acitas, Peter Filzmoser, Birdal Senoglu
In linear regression, the least squares (LS) estimator has certain optimality properties if the errors are normally distributed. This assumption is often violated in practice, part…
Robust multivariate methods in Chemometrics
Peter Filzmoser, Sven Serneels, Ricardo Maronna +1
This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statisti…
A method to identify geochemical mineralization on linear transect
Dominika Mikšová, Christopher Rieser, Peter Filzmoser +2
Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mikšová, Rieser, and Filzmoser…
Identification of mineralization in geochemistry along a transect based on the spatial curvature of log-ratios
Dominika Mikšová, Christopher Rieser, Peter Filzmoser
Detecting subcropping mineralizations but also deeply buried mineralizations is one important goal in geochemical exploration. The identification of useful indicators for mineraliz…
Cellwise Robust M Regression
Peter Filzmoser, Sebastiaan Höppner, Irene Ortner +2
The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear mod…
Robust Principal Component Analysis for Compositional Tables
Julie Rendlová, Karel Hron, Kamila Fačevicová +1
A data table which is arranged according to two factors can often be considered as a compositional table. An example is the number of unemployed people, split according to gender a…
Variable selection with genetic algorithms using repeated cross-validation of PLS regression models as fitness measure
David Kepplinger, Peter Filzmoser, Kurt Varmuza
Genetic algorithms are a widely used method in chemometrics for extracting variable subsets with high prediction power. Most fitness measures used by these genetic algorithms are b…