1 code implementation • 19 Oct 2023 • Jana Gauss, Fabian Scheipl, Moritz Herrmann
Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.
1 code implementation • 1 Jul 2022 • Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: theoretical arguments and empirical evidence show that clustering embedding vectors, representing the structure of a data manifold instead of the observed feature vectors themselves, is highly beneficial.
no code implementations • 1 Jul 2022 • Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
Outlier or anomaly detection is an important task in data analysis.
1 code implementation • 14 Sep 2021 • Moritz Herrmann, Fabian Scheipl
We consider functional outlier detection from a geometric perspective, specifically: for functional data sets drawn from a functional manifold which is defined by the data's modes of variation in amplitude and phase.
no code implementations • 28 Jul 2021 • Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS).
1 code implementation • 11 Mar 2021 • Alexander Volkmann, Almond Stöcker, Fabian Scheipl, Sonja Greven
Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary like precipitation, temperature, and wind speeds over time at a given weather station.
Methodology
1 code implementation • 22 Dec 2020 • Moritz Herrmann, Fabian Scheipl
The contributions of the paper are three-fold: First of all, we define a theoretical framework which allows to systematically assess specific challenges that arise in the functional data context, transfer several nonlinear dimension reduction methods for tabular and image data to functional data, and show that manifold methods can be used successfully in this setting.
1 code implementation • 27 Jun 2020 • Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events.
1 code implementation • 18 Nov 2019 • Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl
In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.
2 code implementations • 4 Jun 2018 • Andreas Bender, Fabian Scheipl
This article introduces the pammtools package, which facilitates data transformation, estimation and interpretation of Piece-wise exponential Additive Mixed Models.
Computation
1 code implementation • 26 May 2011 • Fabian Scheipl, Ludwig Fahrmeir, Thomas Kneib
Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms.
Methodology Applications