RESEARCH INTERESTS

Methodological Research

• Bayesian Inference


• Statistical Learning Methods


• Quantile regression


• Joint Modelling


• Structured Additive Regression

APPLICATIONS

• Cystic Fibrosis


• Nutritional Medicine


• Biodiversity

PROJECTS

• DFG (Advanced Statistical Inference in Joint Models for Longitudinal and Time to Event Data)


• Freigeist Fellowship (Bayesian Boosting - A new approach to data science, unifying two statistical philosophies)


• IZKF Project J61  (Extending joint models in biomedical outcomes)

PUBLICATIONS

1. Gerstmeyer, J., Bergherr, E., & Griesbach, C. (2026). Unbiased random effects estimation in generalized linear mixed models via likelihood-based boosting. AIMS Mathematics, 11(1), 1675–1700.


2. Knieper, L., Hothorn, T., Bergherr, E., & Griesbach, C. (2025). Gradient boosting for generalised additive mixed models. Statistics and Computing, 35(4), 84.


3. Rappl, A., Carlan, M., Kneib, T., Klokman, S., & Bergherr, E. (2025). Bayesian effect selection in structured additive quantile regression. Statistical Modelling, 25(4), 295–322.


4. Daub, A., Mayr, A., Zhang, B., & Bergherr, E. (2025). A balanced statistical boosting approach for GAMLSS via new step lengths. Computational Statistics, 40(8), 4741–4773.


5. Seifert, Q. E., Thielmann, A., Bergherr, E., Säfken, B., Zierk, J., Rauh, M., & Hepp, T. (2025). Penalized regression splines in mixture density networks. The International Journal of Biostatistics, 21(1), 239–253.


6. Balzer, M., Bergherr, E., Hutter, S., & Hepp, T. (2025). Gradient boosting for Dirichlet regression models. AStA – Advances in Statistical Analysis, 1–41.


7. Sabados, A., Kim, C., Rampp, S., Bergherr, E., Buchfelder, M., Schnell, O., & Müller-Voggel, N. (2025). Reducing tinnitus via inhibitory influence of the sensorimotor system on auditory cortical activity. Journal of Neuroscience, 45(17), e0581242025.


8. Griesbach, C., & Bergherr, E. (2024). Additive mixed models for location, scale and shape via gradient boosting techniques. In International Workshop on Statistical Modelling (pp. 218–223). Cham: Springer Nature Switzerland.


9. Cavieres, J., Monnahan, C. C., Bolin, D., & Bergherr, E. (2024). Approximated Gaussian random field under different parameterizations for MCMC. In International Workshop on Statistical Modelling (pp. 204–210). Cham: Springer Nature Switzerland.


10. Thielmann, A., Reuter, A., Seifert, Q., Bergherr, E., & Säfken, B. (2024). Topics in the Haystack: Enhancing topic quality through corpus expansion. Computational Linguistics, 50(2), 619–655.


11. Knieper, L., Kneib, T., & Bergherr, E. (2024). Spatial confounding in gradient boosting. In International Workshop on Statistical Modelling (pp. 88–94). Cham: Springer Nature Switzerland.


12. Zhang, B., Griesbach, C., & Bergherr, E. (2024). Bayesian learners in gradient boosting for linear mixed models. The International Journal of Biostatistics, 20(1), 123–141.


13. Rappl, A., Kneib, T., Lang, S., & Bergherr, E. (2023). Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data. Statistics and Computing, 33(6), 135.


14. Potts, S., Bergherr, E., Reinke, C., & Griesbach, C. (2023). Prediction-based variable selection for component-wise gradient boosting. The International Journal of Biostatistics, 20(1), 293–314.


15. Kneib, T., & Bergherr, E. (2023). Verteilungsregression. In Moderne Verfahren der Angewandten Statistik (pp. 1–22). Berlin, Heidelberg: Springer.


16. Griesbach, C., Mayr, A., & Bergherr, E. (2023). Variable selection and allocation in joint models via gradient boosting techniques. Mathematics, 11(2), 411.


17. Zhang, B., Hepp, T., Greven, S., & Bergherr, E. (2022). Adaptive step-length selection in gradient boosting for Gaussian location and scale models. Computational Statistics, 37(5), 2295–2332.


18. Rappl, A., Mayr, A., & Waldmann, E. (2022). More than one way: Exploring the capabilities of different estimation approaches to joint models for longitudinal and time-to-event outcomes. The International Journal of Biostatistics, 18(1), 127–149.


19. Griesbach, C., Groll, A., & Bergherr, E. (2021). Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques. PLOS ONE, 16(7), e0254178.


20. Griesbach, C., Groll, A., & Bergherr, E. (2021). Joint modelling approaches to survival analysis via likelihood-based boosting techniques. Computational and Mathematical Methods in Medicine, 2021, 4384035.


21. Griesbach, C., Säfken, B., & Waldmann, E. (2021). Gradient boosting for linear mixed models. The International Journal of Biostatistics, 17(2), 317–329.


22. Gayawan, E., Adebayo, S. B., & Waldmann, E. (2019). Modeling the spatial variability in the spread and correlation of childhood malnutrition in Nigeria. Statistics in Medicine, 38(10), 1869–1890.


23. Schink, K., Herrmann, H. J., Schwappacher, R., Meyer, J., Orlemann, T., Waldmann, E., … Zopf, Y. (2018). Effects of whole-body electromyostimulation combined with individualized nutritional support on body composition in patients with advanced cancer: A controlled pilot trial. BMC Cancer, 18, 886.


24. Waldmann, E. (2018). Quantile regression: A short story on how and why. Statistical Modelling, 18(3–4), 203–218.


25. Pinzer, T. C., Tietz, E., Waldmann, E., Schink, M., Neurath, M. F., & Zopf, Y. (2018). Circadian profiling reveals higher histamine plasma levels and lower diamine oxidase serum activities in 24% of patients with suspected histamine intolerance compared to food allergy and controls. Allergy, 73(4), 949–957.


26. Scheel, J. F., Schieber, K., Reber, S., Stoessel, L., Waldmann, E., Jank, S., … Erim, Y. (2018). Psychosocial variables associated with immunosuppressive medication non-adherence after renal transplantation. Frontiers in Psychiatry, 9, 23.


27. Scheel, J., Reber, S., Stoessel, L., Waldmann, E., Jank, S., Eckardt, K. U., … Erim, Y. (2017). Patient-reported non-adherence and immunosuppressant trough levels are associated with rejection after renal transplantation. BMC Nephrology, 18(1), 107.


28. Waldmann, E., Sobotka, F., & Kneib, T. (2017). Bayesian regularisation in geoadditive expectile regression. Statistics and Computing, 27(6), 1539–1553.


29. Blagrove, M. S., Caminade, C., Waldmann, E., Sutton, E. R., Wardeh, M., & Baylis, M. (2017). Co-occurrence of viruses and mosquitoes at the vectors’ optimal climate range: An underestimated risk to temperate regions? PLOS Neglected Tropical Diseases, 11(6), e0005604.


30. Mayr, A., Hofner, B., Waldmann, E., Hepp, T., Meyer, S., & Gefeller, O. (2017). An update on statistical boosting in biomedicine. Computational and Mathematical Methods in Medicine, 2017, 6083072.


31. Gefeller, O., Hofner, B., Mayr, A., & Waldmann, E. (2017). Predictive modelling based on statistical learning in biomedicine. Computational and Mathematical Methods in Medicine, 2017, 4041736.


32. Waldmann, E., Taylor-Robinson, D., Klein, N., Kneib, T., Pressler, T., Schmid, M., & Mayr, A. (2017). Boosting joint models for longitudinal and time-to-event data. Biometrical Journal, 59(6), 1104–1121.


33. Alanin, M. C., Aanaes, K., Høiby, N., Pressler, T., Skov, M., Nielsen, K. G., Taylor-Robinson, D., Krogh Johansen, H., von Buchwald, C., & Waldmann, E. (2016). Sinus surgery postpones chronic Gram-negative lung infection: Cohort study of 106 patients with cystic fibrosis. Rhinology, 54(3), 206–213.


34. Hepp, T., Schmid, M., Gefeller, O., Waldmann, E., & Mayr, A. (2016). Approaches to regularized regression: A comparison between gradient boosting and the lasso. Methods of Information in Medicine, 55(5), 422–430.


35. Qvist, T., Taylor-Robinson, D., Waldmann, E., Olesen, H. V., Hansen, C. R., Mathiesen, I. H., … Pressler, T. (2016). Comparing the harmful effects of nontuberculous mycobacteria and Gram-negative bacteria on lung function in patients with cystic fibrosis. Journal of Cystic Fibrosis, 15(3), 380–385.


36. Salvi, R., Steigleder, T., Schlachetzki, J. C., Waldmann, E., Schwab, S., Winner, B., … Kohl, Z. (2016). Distinct effects of chronic dopaminergic stimulation on hippocampal neurogenesis and striatal doublecortin expression in adult mice. Frontiers in Neuroscience, 10, 77.


37. Waldmann, E., & Kneib, T. (2015). Variational approximations in geoadditive latent Gaussian regression: Mean and quantile regression. Statistics and Computing, 25(6), 1247–1263.


38. Stein, A., Beck, J., Meyer, C., Waldmann, E., Weigelt, P., & Kreft, H. (2015). Differential effects of environmental heterogeneity on global mammal species richness. Global Ecology and Biogeography, 24(9), 1072–1083.


39. Waldmann, E., & Kneib, T. (2015). Bayesian bivariate quantile regression. Statistical Modelling, 15(4), 326–344.


40. Waldmann, E., Kneib, T., Yue, Y. R., Lang, S., & Flexeder, C. (2013). Bayesian semiparametric additive quantile regression. Statistical Modelling, 13(3), 223–252.


41. Waldmann, E., & Kneib, T. (2011). Bayesian structured additive quantile regression. International Workshop on Statistical Modelling, 648.