Software & Publications

Software

• refund: penalized additive regression models for functional and scalar covariates and functional responses.
• gamm4: generalized additive mixed models using 'mgcv' and 'lme4'.
• tidyfun: data wrangling and exploratory analysis for functional data.
• spikeSlabGAM: Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via SSVS (stochastic search variable selection) with spike-and-slab priors.
• RLRsim: implementing exact (Restricted) Likelihood Ratio Tests for variance components and nonparametric terms, with interfaces for models fit with nlme::lme(), lme4::lmer(), mgcv::gamm() or SemiPar:spm().
• pammtools: Piece-wise exponential additive mixed model tools.

Selected Publications

• Herrmann, M., Pfisterer, F., & Scheipl, F. (2023). A geometric framework for outlier detection in high-dimensional data. WIREs Data Mining and Knowledge Discovery, e1491.
• Hartl, W. H., Kopper, P., Bender, A., Scheipl, F., Day, A. G., Elke, G., Küchenhoff, H. (2022). Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models. Critical Care, 26(1), 1-12.
• Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., Bischl, B. (2021). Developing Open Source Educational Resources for Machine Learning and Data Science. arXiv preprint arXiv:2107.14330.
• Herrmann, M., Scheipl, F. (2021). A geometric perspective on functional outlier detection. Stats, 4(4), 971-1011.
• Gertheiss, J., Scheipl, F., Lauer, T., Ehrhardt, H. (2021). Statistical inference for ordinal predictors in generalized linear and additive models with application to bronchopulmonary dysplasia. arXiv preprint arXiv:2102.01946.
• Bauer, A., Scheipl, F., Küchenhoff, H., Gabriel, A. A. (2021). Registration for Incomplete Non-Gaussian Functional Data. arXiv preprint arXiv:2108.05634.
• Volkmann, A., Stöcker, A., Scheipl, F., Greven, S. (2021). Multivariate functional additive mixed models. Statistical Modelling, 1471082X211056158.
• Herrmann, M., Scheipl, F. (2020). Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. arXiv preprint arXiv:2012.11987.
• Bender, A., Rügamer, D., Scheipl, F., Bischl, B. (2020). A general machine learning framework for survival analysis. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 158-173). Springer, Cham.
• Greven, S., Scheipl, F. (2020). Comments on: Inference and computation with Generalized Additive Models and their extensions. TEST, 29(2), 343-350.
• Pfisterer, F., Beggel, L., Sun, X., Scheipl, F., Bischl, B. (2019). Benchmarking time series classification--Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511
• Happ C, Scheipl F, Gabriel A-A, Greven S (2019): A General Framework for Multivariate Functional Principal Component Analysis of Amplitude and Phase Variation. Stat, 8(1): e220.
• Hartl, W. H., Bender, A., Scheipl, F., Kuppinger, D., Day, A. G., Küchenhoff, H. (2019). Calorie intake and short-term survival of critically ill patients. Clinical Nutrition, 38(2), 660-667.
• Bender A, Scheipl F, Hartl W, Day AG, Küchenhoff H (2019): Penalized estimation of complex, non-linear exposure-lag-response associations. Biostatistics, 20(2): 315-331.
• Cederbaum J, Scheipl F, Greven S (2018): Fast symmetric additive covariance smoothing. Computational Statistics and Data Analysis, 120: 25-41.
• Bauer A, Scheipl F, Küchenhoff H, Gabriel A-A (2018): An introduction to semiparametric function-on-scalar regression. Statistical Modelling, 18(3-4): 346-64.
• Bender A, Groll A, Scheipl F (2018). A generalized additive model approach to time-to-event analysis. Statistical Modelling. Statistical Modelling, 18(3-4): 299-321.
• Greven S, Scheipl F (2017): A General Framework for Functional Regression Modelling. Invited discussion paper. Statistical Modelling, 17(1-2): 1-35.
  
• Gasparrini A, Scheipl F, Armstrong B, Kenward MG (2017): A penalized framework for distributed lag non‐linear models. Biometrics, 73(3): 938-48.
• Greven S, Scheipl F (2016): Comment on "Smoothing parameter and model selection for general smooth models". Journal of the American Statistical Association, 111(516): 1568-1573
• Scheipl F, Gertheiss J, Greven S (2016): Generalized Functional Additive Mixed Models. Electronic Journal of Statistics, 10(1):1455-1492.
• Scheipl F, Greven S (2016): Identifiability in penalized function-on-function regression models. Electronic Journal of Statistics, 10(1):495-526.
• Scheipl F, Staicu A-M, Greven S (2015): Functional Additive Mixed Models. Journal of Computational and Graphical Statistics, 24(2), 477-501. arXiv
• Brockhaus S, Scheipl F, Hothorn T und Greven S (2015): The Functional Linear Array Model. Statistical Modelling, 15(3): 279-300.
• Ivanescu AE, Staicu A-M, Scheipl F and Greven S (2015): Penalized function-on-function regression. Computational Statistics, 30(2): 539-568.
• Fuchs K, Scheipl F and Greven S (2015): Penalized scalar-on-functions regression with Interaction Term. Computational Statistics & Data Analysis, 81: 38-51.
• Obermaier V, Scheipl F, Heumann C, Wassermann J, Küchenhoff H (2015): Flexible Distributed Lags for Modeling Earthquake Data. Journal of the Royal Statistical Society, Series C, 64(2): 395-412.
• McLean M, Hooker G, Staicu A-M, Scheipl F and Ruppert D (2014): Functional Generalized Additive Models. Journal of Computational and Graphical Statistics, 23(1): 249-269.
• Goldsmith J and Scheipl F (2013): Estimator Selection and Combination in Scalar-on-Function Regression. Computational Statistics and Data Analysis, 70: 362-372.
• Greven S and Crainiceanu C (2013): On Likelihood Ratio Testing for Penalized Splines. AStA Advances in Statistical Analysis, 97(4): 387-402.
• Scheipl F, Kneib T, Fahrmeir L (2013): Penalized Likelihood and Bayesian Function Selection in Regression Models. AStA Advances in Statistical Analysis, 97(4): 349-385. arXiv
• Wood SN, Scheipl F, Faraway JJ (2013): Straightforward intermediate rank tensor product smoothing in mixed models. Statistics and Computing, 23(3):341-360.