Coors, Schalk, Bischl,
Rügamer (2021): AutoCompboost: Automatic Interpretable Boosting. Accepted at ECML-PKDD Workshop on Automating Data Science (ADS2021)
Rügamer, et al. (2021): deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Liew, Lee,
Rügamer, Nunzio, Heneghan, Falla, Evans (2021): A novel metric of reliability in pressure pain threshold measurement. Accepted at Scientific Reports - Nature Goschenhofer, Hvingelby
Rügamer, Thomas, Wagner, Bischl (2021): Deep Semi-Supervised Learning for Time Series Classification. Under Revision. Fritz, Dorigatti,
Rügamer (2021): Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Under Revision. Kopper, Pölsterl, Wachinger, Bischl, Bender,
Rügamer (2020): Semi-Structured Deep Piecewise Exponential Models. Accepted at the AAAI Spring Symposium 2021. Baumann, Hothorn,
Rügamer (2020): Deep Conditional Transformation Models. Accepted at ECML-PKDD 2021. Liew, Peolsson,
Rügamer, Wibault, Löfgren, Dedering, Zsigmond, Falla (2020): Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy - a machine learning approach. Accepted at Scientific Reports - Nature
Rügamer, Pfisterer, Bischl (2020): Neural Mixture Density Regression. Under Revision. Bender,
Rügamer, Scheipl, Bischl (2020): A General Machine Learning Framework for Survival Analysis. ECML-PKDD 2020. Berninger, Stöcker,
Rügamer (2020): A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Accepted at the Journal of Forecasting.
Rügamer, Baumann, Greven (2020): Selective Inference for Additive and Mixed Models. Under Revision.
Rügamer, Kolb and Klein (2020): A Unified Network Architecture for Semi-Structured Deep Distributional Regression. Under Revision. Liew,
Rügamer, Abichandani, De Nunzio (2020): Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting. Gait & Posture, 80, 90-95 Liew,
Rügamer, De Nunzio, Falla (2020): Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal 29, 1845 -- 1859. Liew,
Rügamer, Stöcker, De Nunzio (2020): Classifying neck pain status using scalar and functional biomechanical variables - development of a method using functional data boosting. Gait and Posture 76, 146-150.
Rügamer, Greven (2020): Inference for L2-Boosting. Statistics and Computing volume 30, 279-289. Brockhaus,
Rügamer, Greven (2020): Boosting Functional Regression Models with FDboost. Journal of Statistical Software, 94 (10), 1-50.
Rügamer, Greven (2018): Selective inference after likelihood- or test-based model selection in linear models. Statistics & Probability Letters. Volume 140, Pages 7-12. Säfken,
Rügamer, Kneib, Greven (2018): Conditional Model Selection in Mixed-Effects Models with cAIC4. To appear in the Journal of Statistical Software.
Rügamer, Brockhaus, Gentsch, Scherer, Greven (2018): Boosting Factor-Specific Functional Historical Models for the
Detection of Synchronisation in Bioelectrical Signals. Journal of the Royal Statistical Society, Series C, 67: 621-642. Klüser, Holler, Simak, Tater, Smets,
Rügamer, Küchenhoff, Wess (2016): Predictors of Sudden Cardiac Death in Doberman Pinschers with Dilated Cardiomyopathy. Journal of Veterinary Internal Medicine, volume 3, number 3, 722--732. Gillhuber,
Rügamer, Pfister, Scheuerle (2014). Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany. BMC Research Notes, 7, 112. Software (R-packages)
Semi-Strutured Deep Distrobutional Regression on Github
SELective inference For Mixed and ADditive model Estimators on Github and soon on CRAN.
Boosting Functional Regression Models on CRAN and github
Conditional Akaike Information Criterion for ‘lme4’ on CRAN and Github
Inference for Model-based Boosting on Github
Conditional Inference after Likelihood-based Selection on Github
efficient operations on row-wise tensor product linked evaluations with special structures on Github Talks, Workshops and Research Stays
18. March ‘21 (Leuven, BE) Sampling-based Approaches for Valid Post-Selection Inference Seminar Series in Statistics, KU Leuven
28. October ‘20 (Dortmund, GER) Semi-Structured Deep Distributional Regression. Regression approaches for large-scale high-dimensional data, Dortmund Data Science Centers
15. October ‘20 (Bonn, GER) Semi-Structured Deep Distributional Regression. (Virtual) Institutskolloquium IMBIE
29. July ‘20 (Munich, GER) Semi-Structured Deep Distributional Regression. Virtual Workshop by the Munich Center of Machine Learning
26. February ‘20 (Munich, GER) Semi-Structured Deep Distributional Learning. Institutskolloquium, Department of Statistics, LMU Munich
January ‘20 (Xiamen, CHN) Short research stay and exchange with several people involved in the DFG RTG group High Dimensional Nonstationary Time Series
21. March ‘19 (Munich, GER) Inference for L2-Boosting, DAGSTAT 2019, Machine Learning Session
15. June ‘18 (Munich, GER) Estimation, Model Choice and Subsequent Inference: Methods for Additive and Functional Regression Models. Doctoral Dissertation Defense
26. - 28. March 2018 (Frankfurt a.M., GER) Boosting Factor-Specific Functional Historical Models. Award Session, 64th Biometric Colloquium
16. - 18. December 2017 (London, UK) Selective Inference for L2-Boosting. 10th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2017)
23. - 25. July 2017 (Reisensburg, GER) Boosting functional regression models with FDboost. Statistical Computing 2017
9. - 11. December 2016 (Seville, ESP) Boosting Factor-Specific Functional Historical Models. 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016)
30. November 2016 (Munich, GER) Selective Inference for L2-Boosting. Institutskolloquium, Department of Statistics LMU, Munich