• Bayesian statistics
• Regression methods for large high-dimensional data
• Spatio-temporal point process modeling in biostatistics
• Gaussian process modeling with various applications

Selected publications

Geppert, L., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). Streaming statistical models via Merge & Reduce.   International journal of data science and analytics, 10(4), 331–347. https://doi.org/10.1007/s41060-020-00226-0

Madjar, K., Zucknick, M., Ickstadt, K., & Rahnenführer (2020). Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression. arXiv:2004.07542v1

Ickstadt, K., Schäfer, M., & Zucknick, M. (2018). Toward integrative Bayesian analysis in molecular biology. Annual review of statistics and its application, 5, 141–167. https://doi.org/10.1146/annurev-statistics-031017-100438

Geppert, L., Ickstadt, K., Munteanu, A., Quedenfeld, J., & Sohler, C. (2017). Random projections for Bayesian regression. Statistics and computing, 27(1), 79–101. https://doi.org/10.1007/s11222-015-9608-z

Treppmann, T., Ickstadt, K., & Zucknick, M. (2017). Integration of multiple genomic data sources in a Bayesian cox model for variable selection and prediction. Computational and mathematical methods in medicine, 2017. https://doi.org/10.1155/2017/7340565