1 code implementation • 29 Nov 2023 • Pia Pfeiffer, Andreas Alfons, Peter Filzmoser
This paper investigates how such procedures can be used for robust sparse association estimators.
1 code implementation • 12 Jun 2023 • Lukas Neubauer, Peter Filzmoser
A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain.
no code implementations • 11 May 2023 • Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz, Peter Filzmoser, Martin Jech
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-the-art online CPD routines which it outperforms in terms of false positive rate and out-of-control average run length.
no code implementations • 18 Oct 2022 • 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 originating from single variables.
no code implementations • 9 Mar 2022 • Georg Heiler, Thassilo Gadermaier, Thomas Haider, Allan Hanbury, Peter Filzmoser
Good quality network connectivity is ever more important.
no code implementations • 25 Jan 2022 • 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 contributions.
2 code implementations • 17 Nov 2017 • David Kepplinger, Peter Filzmoser, Kurt Varmuza
Due to multicollinearity, partial least squares regression is often more appropriate, but rarely considered in genetic algorithms due to the additional cost for estimating the optimal number of components.
Computation