Prof. Dr. Jannis Kück

Visiting Scholar at University of Fribourg, Switzerland (Chair of Applied Econometrics), Prof. Martin Huber

Ph. D. in Statistics, Faculty of Business Administration (University of Hamburg), Prof. Martin Spindler

Visiting Scholar at University of California, Irvine (Department of Economics/Deep Data Lab), Prof. Matthew Harding

• Chernozhukov, V., Klaaßen, S., Kueck J., Spindler, M. (2022): Uniform Inference in High-Dimensional Gaussian Graphical Models. (Biometrika, available here)
• Kueck, J., Luo, Y., Spindler, M., Wang, Z. (2022): Estimation and Inference of Treatment Effects with L2-Boosting in High-Dimensional Settings. (Journal of Econometrics, available here)
• Felderer, B., Kueck, J., Spindler, M. (2022): Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-mode Online Panel (Social Science Computer Review, available here).
• Klaaßen, S., Kueck, J., Spindler, M. (2021): Transformation Models in High Dimensions. (Journal of Business & Economic Statistics, available here)
• Kueck, J. (2020): Advances in Machine Learning: Valid Inference about High-Dimensional Parameters. (available here) Dissertation, Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky.

• Bach, P.,  Klaaßen, S., Kueck, J., Spindler, M. (2020): Uniform Inference in High-Dimensional Additive Models. (R&R at Journal of Econometrics, available here)
• Luo, Y., Spindler, M., Kueck, J. (2022): High-Dimensional L2-Boosting: Rate of Convergence. (R&R at Journal of Machine Learning Research, available here)
• Huber, M., Kueck, J. (2022): Testing the Identification of Causal Effects in Observational Data. (available here)
• Transformed Failure Time Models in High-Dimensions (with Oliver Schacht)
• Adaptive Smoothing for Nonparametric Estimation (with Ye Luo and Martin Spindler)
• Double Machine Learning for Partial Correlations and Partial Copulas (with Malte Kurz)

• High dimensional statistics
• Econometrics
• Causal Inference
• Machine Learning
• Deep Learning
• Graphical Models