24 citations · 72 across the 24 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Clustering of imbalanced high-dimensional media data
Sarka Brodinova, Maia Zaharieva, Peter Filzmoser +2
Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups dif…
Robust and sparse k-means clustering for high-dimensional data
Sarka Brodinova, Peter Filzmoser, Thomas Ortner +2
In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is…
Multigroup discrimination based on weighted local projections
Thomas Ortner, Irene Hoffmann, Peter Filzmoser +3
A novel approach for supervised classification analysis for high dimensional and flat data (more variables than observations) is proposed. We use the information of class-membershi…
Local projections for high-dimensional outlier detection
Thomas Ortner, Peter Filzmoser, Maia Zaharieva +2
In this paper, we propose a novel approach for outlier detection, called local projections, which is based on concepts of Local Outlier Factor (LOF) (Breunig et al., 2000) and RobP…
Robust and sparse estimation methods for high dimensional linear and logistic regression
Fatma Sevinc Kurnaz, Irene Hoffmann, Peter Filzmoser
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression. The algorithms to compute the estimators are based on the idea of repeatedly a…
Guided projections for analysing the structure of high-dimensional data
Thomas Ortner, Peter Filzmoser, Maia Zaharieva +2
A powerful data transformation method named guided projections is proposed creating new possibilities to reveal the group structure of high-dimensional data in the presence of nois…