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Research

We are interested in the development of new methodology in the field of computational statistics and in the applied analysis of biomedical and epidemiological data.

• Methodological research
  • Statistical boosting
  • GAMLSS (generalized additive models for location, scale and shape)
  • Quantile regression
  • Prediction inference and prediction intervals
  • Variable selection for high-dimensional data
• Applications 
  • Clinical and genetical prediction models
  • Paediatric and Perinatal Epidemiology
  • Eating disorders
  • Schizophrenia and depression
  • Genomics 

Projects 

• DFG Project Boosting polygenic risk scores via distributional regression to uncover potential gene-environment interactions together with Dr. Carlo Maj, Philipps-University Marburg. 
• DFG Project Boosting copulas – multivariate distributional regression for digital medicine together with Prof. Nadja Klein, Karlsruhe Institute for Technology. 

Most Recent Publications

• Briseno Sanchez G, Klein N, Klinkhammer H and Mayr A (2025): Boosting Distributional Copula Regression for Bivariate Binary, Discrete and Mixed Responses. Statistical Methods for Medical Research. (accepted). 
• Klinkhammer H, Staerk C, Maj C, Krawitz P and Mayr A (2024). Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions. Statistics in Medicine. 43 (28), 5412-5430.
• Staerk C, Byrd A and Mayr A (2024): Recent methodological trends in Epidemiology: No need for data-driven variable selection? American Journal of Epidemiology. 193(2): 370-376.
• Staerk C, Klinkhammer H, Wistuba T, Maj C and Mayr A (2024): Generalizability of polygenic prediction models: how is the R² defined on test data? BMC Medical Genomics. 17(1): 132.
• Strömer A, Klein N, Staerk C, Klinkhammer H and Mayr A (2023): Boosting Multivariate Structured Additive Distributional Regression Models. Statistics in Medicine. 42(11): 1779-1801.