Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. - Fabian Scheipl; Sonja Greven; Helmut Küchenhoff (Comput. Stat. Data Anal., 2008)

Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior. - Fabian Scheipl; Thomas Kneib (Comput. Stat. Data Anal., 2009)

Bayesian regularization and model choice in structured additive regression. - Fabian Scheipl (2011)

Straightforward intermediate rank tensor product smoothing in mixed models. - Simon N. Wood; Fabian Scheipl; Julian J. Faraway (Stat. Comput., 2013)

Estimator selection and combination in scalar-on-function regression. - Jeff Goldsmith; Fabian Scheipl (Comput. Stat. Data Anal., 2014)

Penalized scalar-on-functions regression with interaction term. - Karen Fuchs; Fabian Scheipl; Sonja Greven (Comput. Stat. Data Anal., 2015)

Penalized function-on-function regression. - Andrada E. Ivanescu; Ana-Maria Staicu; Fabian Scheipl; Sonja Greven (Comput. Stat., 2015)

Fast symmetric additive covariance smoothing. - Jona Cederbaum; Fabian Scheipl; Sonja Greven (Comput. Stat. Data Anal., 2018)

Benchmarking time series classification - Functional data vs machine learning approaches. - Florian Pfisterer; Laura Beggel; Xudong Sun 002; Fabian Scheipl; Bernd Bischl (CoRR, 2019)

A General Machine Learning Framework for Survival Analysis. - Andreas Bender 001; David Rügamer; Fabian Scheipl; Bernd Bischl (ECML/PKDD (3), 2020)

Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. - Moritz Herrmann; Fabian Scheipl (CoRR, 2020)

A geometric perspective on functional outlier detection. - Moritz Herrmann; Fabian Scheipl (CoRR, 2021)

Developing Open Source Educational Resources for Machine Learning and Data Science. - Ludwig Bothmann; Sven Strickroth; Giuseppe Casalicchio; David Rügamer; Marius Lindauer; Fabian Scheipl; Bernd Bischl (Teaching ML, 2022)

DCSI - An improved measure of cluster separability based on separation and connectedness. - Jana Gauss; Fabian Scheipl; Moritz Herrmann (CoRR, 2023)

A geometric framework for outlier detection in high-dimensional data. - Moritz Herrmann; Florian Pfisterer; Fabian Scheipl (WIREs Data. Mining. Knowl. Discov., 2023)

Enhancing cluster analysis via topological manifold learning. - Moritz Herrmann; Daniyal Kazempour; Fabian Scheipl; Peer Kröger (Data Min. Knowl. Discov., 2024)