Fabian Scheipl

21 papers Journal 18Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Area-based epigraph and hypograph indices for functional outlier detection.
Comput. Stat.
Belén Pulido, Alba M. Franco Pereira, Rosa E. Lillo, Fabian Scheipl
2024 J jnl
Enhancing cluster analysis via topological manifold learning.
Data Min. Knowl. Discov.
Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger
2023 J jnl
A geometric framework for outlier detection in high-dimensional data.
WIREs Data. Mining. Knowl. Discov.
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
2023 J jnl
DCSI - An improved measure of cluster separability based on separation and connectedness.
CoRR
Jana Gauss, Fabian Scheipl, Moritz Herrmann
2022 J jnl
A geometric framework for outlier detection in high-dimensional data.
CoRR
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
2022 — conf
Developing Open Source Educational Resources for Machine Learning and Data Science.
Teaching ML
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2022 J jnl
Enhancing cluster analysis via topological manifold learning.
CoRR
Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger
2021 J jnl
A geometric perspective on functional outlier detection.
CoRR
Moritz Herrmann, Fabian Scheipl
2021 J jnl
Developing Open Source Educational Resources for Machine Learning and Data Science.
CoRR
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2020 — conf
A General Machine Learning Framework for Survival Analysis.
ECML/PKDD (3)
Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020 J jnl
A General Machine Learning Framework for Survival Analysis.
CoRR
Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020 J jnl
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
CoRR
Moritz Herrmann, Fabian Scheipl
2019 J jnl
Benchmarking time series classification - Functional data vs machine learning approaches.
CoRR
Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl
2018 J jnl
Fast symmetric additive covariance smoothing.
Comput. Stat. Data Anal.
Jona Cederbaum, Fabian Scheipl, Sonja Greven
2015 J jnl
Penalized function-on-function regression.
Comput. Stat.
Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven
2015 J jnl
Penalized scalar-on-functions regression with interaction term.
Comput. Stat. Data Anal.
Karen Fuchs, Fabian Scheipl, Sonja Greven
2014 J jnl
Estimator selection and combination in scalar-on-function regression.
Comput. Stat. Data Anal.
Jeff Goldsmith, Fabian Scheipl
2013 J jnl
Straightforward intermediate rank tensor product smoothing in mixed models.
Stat. Comput.
Simon N. Wood, Fabian Scheipl, Julian J. Faraway
2011 — —
Bayesian regularization and model choice in structured additive regression.
Fabian Scheipl
2009 J jnl
Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior.
Comput. Stat. Data Anal.
Fabian Scheipl, Thomas Kneib
2008 J jnl
Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models.
Comput. Stat. Data Anal.
Fabian Scheipl, Sonja Greven, Helmut Küchenhoff