Dr. Jannis Kück

Forschungsinteressen

• Hochdimensionale Statistik
• Ökonometrie
• Kausale Inferenz
• Maschinelles Lernen
• Deep Learning
• Graphische Modelle

Curriculum Vitae (available here)

Publikationen

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., Spindlerm 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.

Working Papers

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)

Work in Progress

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).

Lehrveranstaltungen

Akademische Ausbildung