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ORCIDFabian Scheipl
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- affiliation: LMU Munich, Department of Statistics, Faculty of Mathematics, Computer Science and Statistics, Germany
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2020 – today
- 2024
[j9]Moritz Herrmann
, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. Data Min. Knowl. Discov. 38(3): 840-887 (2024)- 2023
- [j8]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. WIREs Data. Mining. Knowl. Discov. 13(3) (2023) - [i8]Jana Gauss, Fabian Scheipl, Moritz Herrmann:
DCSI - An improved measure of cluster separability based on separation and connectedness. CoRR abs/2310.12806 (2023) - 2022
- [c2]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML 2022: 1-6 - [i7]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. CoRR abs/2207.00367 (2022) - [i6]Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. CoRR abs/2207.00510 (2022) - 2021
- [i5]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021) - [i4]Moritz Herrmann, Fabian Scheipl:
A geometric perspective on functional outlier detection. CoRR abs/2109.06849 (2021) - 2020
- [c1]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. ECML/PKDD (3) 2020: 158-173 - [i3]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. CoRR abs/2006.15442 (2020) - [i2]Moritz Herrmann, Fabian Scheipl:
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. CoRR abs/2012.11987 (2020)
2010 – 2019
- 2019
- [i1]Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl:
Benchmarking time series classification - Functional data vs machine learning approaches. CoRR abs/1911.07511 (2019) - 2018
- [j7]Jona Cederbaum, Fabian Scheipl, Sonja Greven:
Fast symmetric additive covariance smoothing. Comput. Stat. Data Anal. 120: 25-41 (2018) - 2015
- [j6]Karen Fuchs, Fabian Scheipl, Sonja Greven:
Penalized scalar-on-functions regression with interaction term. Comput. Stat. Data Anal. 81: 38-51 (2015) - [j5]Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven:
Penalized function-on-function regression. Comput. Stat. 30(2): 539-568 (2015) - 2014
- [j4]Jeff Goldsmith, Fabian Scheipl:
Estimator selection and combination in scalar-on-function regression. Comput. Stat. Data Anal. 70: 362-372 (2014) - 2013
- [j3]Simon N. Wood, Fabian Scheipl, Julian J. Faraway:
Straightforward intermediate rank tensor product smoothing in mixed models. Stat. Comput. 23(3): 341-360 (2013) - 2011
- [b1]Fabian Scheipl:
Bayesian regularization and model choice in structured additive regression. Ludwig Maximilians University Munich, 2011
2000 – 2009
- 2009
- [j2]Fabian Scheipl, Thomas Kneib:
Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior. Comput. Stat. Data Anal. 53(10): 3533-3552 (2009) - 2008
- [j1]Fabian Scheipl, Sonja Greven, Helmut Küchenhoff:
Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Comput. Stat. Data Anal. 52(7): 3283-3299 (2008)
Coauthor Index
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