… to my website. I am an Associate Professor of Statistics at the University of Bonn.

My research interests focus on functional data analysis, semi- and nonparametric statistics, longitudinal data analysis, mathematical and computational statistics. I pursue an application oriented research approach focusing on actual real data problems that are both statistically interesting and practically relevant.

Starting points of my past and current research are data challenges, for instance, in empirical economics, energy economics, finance, e-commerce (e.g., Google AdWords data), and psychology. I am committed to providing the computational implementations of my statistical research and publish R-packages at CRAN and GitHub accompanying my research papers.



Publications in Statistics

Book Chapters

  • Liebl, D. and Reimherr, M. (2020). Simultaneous Inference for Function-valued Parameters: a Fast and Fair Approach. Functional and High-Dimensional Statistics and Related Fields. Edited by: Aneiros, G., Horová, I., Hužková, M. and Vieu, P., pp. 153-159

  • Walders, F. and Liebl, D. (2017). Parameter regimes in partially functional linear regression for panel data. Functional Statistics and Related Fields. Edited by: Aneiros, G., Bongiorno, E. G., Cao, R., Vieu, P., pp. 261-270

  • Liebl, D. and Kneip, A. (2014). Modelling electricity prices as functional data on random domains. Contributions in Infinite-Dimensional Statistics and Related Topics. Edited by: Bongiorno, E. G., Salinelli, E., Goia, A., Vieu, P., pp. 90-97

  • Liebl, D., Mosler, K. and Willwacher, S. (2012). Robust clustering of joint moment curves. 9. Symposium der Sektion Sportinformatik der Deutschen Vereinigung für Sportwissenschaft. Edited by: Byshko, R., Dahmen, T., Gratkowski, M., Gruber, M., Quintana, J., Saupe, D., Vieten, M., Woll, A., pp. 68-73

Further Publications

PhD Thesis

Open Reviews

Submitted Working Papers

Work in Progress

  • Bada, O. and Liebl, D. A subsampled penalty criterion to estimate the number of non-vanishing common factors in large panels

  • Liebl, D. and Mensinger, T. On the minor role of bandwidth selection when smoothing sparse to dense functional data

  • Gider, J. and Liebl, D. Observe the unobservable: Firm value and firm-specific heterogeneity in time trends

  • Liebl, D. and Reimherr, M. Fast and fair simultaneous confidence regions for functional parameters over arbitrary domains

  • Hörmann, S., Kraus, D., and Liebl, D. Testing the MCAR-assumption for partially observed functional data

Teaching Portfolio

Excerpt of past and current courses:

Editorial Service


Journal of the Royal Statistical Society:~Series B (JRSSB), Biometrika, Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS), Journal of the Royal Statistical Society:~Series C (JRSSC), Journal of Time Series Analysis (JTSA), Computational Statistics & Data Analysis (CSDA), Statistical Modeling: An International Journal (SMIJ), Advances in Statistical Analysis (AStA), Econometrics and Statistics (ECOSTA), Advances in Data Analysis and Classification (ADAC), Journal of Computational and Graphical Statistics (JCGS), Journal of Applied Statistics (JAS), Energy Economics (ENEECO), International Journal of Forecasting (IJF), Journal of Statistical Planning and Inference (JSPI), Journal of Multivariate Analysis (JMVA), Statistica Sinica (SS), Statistics and Probability Letters (STAPRO)


  • Together with my co-author Oualid Bada, I am the creator and maintainer of the R-package phtt. The package provides estimation procedures for panel data with general forms of unobservable heterogeneous effects.

  • R-package FunRegPoI

  • R-package PartiallyFD

  • R-package ReconstPoFD

  • R-package fdapoi

  • R-package ffscb

Institutions & Affiliations

University of Bonn.

Hausdorff Center for Mathematics.


  • My former life took (mostly) place in dojos, where I used to wear a judogi.