I am Assistant Professor of Statistics at the University of Bonn.

My research interests focus on functional data analysis, semi- and nonparametric statistics, and panel data analysis.

Bio

2014-present: Assistant Professor of Statistics at the Institute for Financial Economics and Statistics, University of Bonn
2013-2014: Postdoctoral Researcher at the European Center for Advanced Research in Economics and Statistics, Université Libre de Bruxelles
2010-2013: Research Assistant at the Institute for Econometrics and Statistics, University of Cologne
2008-2010: PhD Fellow at the Cologne Graduate School

Publications

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
Liebl, D. (2013). Modeling and forecasting electricity prices: A functional data perspective. The Annals of Applied Statistics, 7(3): 1562-1592
Bada, O. and Liebl, D. (2014). The R-package phtt: Panel data analysis with heterogeneous time trends. Journal of Statistical Software, 59(6): 1-33
Liebl, D., Willwacher, S., Hamill, J. and Brüggemann, G. P. (2014). Ankle plantarflexion strength in rearfoot and forefoot runners: A novel clusteranalytic approach. Human Movement Science, 35: 104-125
Liebl, D., and Kneip, A. (2014). Modelling electricity prices as functional data on random domains. Contributions in Infinite-Dimensional Statistics and Related Topics, Proceedings of the 3rd International Workshop on Functional and Operatorial Statistics. Edited by: Bongiorno, E.G., Salinelli, E., Goia, A., Vieu, P., pp. 90-97
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

Working Papers

Liebl, D. (2017). Inference for functional data with covariate adjustments: From sparse to dense and everything in-between (Revise and Resubmit at the Journal of Multivariate Analysis)
Liebl, D., Rameseder, S., and Rust, C. (2017). Functional insights into Google AdWords (Submitted)
Liebl, D. (2017). Finite sample correction for two-sample inference with sparse covariate-adjusted functional data (Submitted)
Liebl, D., and Walders, F. (2017). Parameter regimes in partial functional panel regression (Submitted)
Kneip, A., and Liebl, D. (2017). On the optimal reconstruction of partially observed functional data

Work in Progress

A subsampled penalty criterion to estimate the number of non-vanishing common factors in large panels (with O. Bada)
Take of your shoes! Analyzing the foot strike behavior of habitual shod runners when running barefoot (with S. Willwacher, J. Hamill, and G. P. Brüggemann)
On the minor role of bandwidth selection when smoothing sparse to dense functional data
Generalized functional linear models with points of impact (with D. Poß, A. Kneip, H. Eisenbarth, and T. Wager)
Observe the Unobservable: Firm Value and Firm-Specific Heterogeneity in Time Trends (with J. Gider)
Life-cycle Wage Trajectories with Missing Data: A Factor Analytic Approach (with P. Pinger)

Teaching Portfolio

Advanced Statistics, Master course at the German Sport University Cologne
Main Textbook: An Introduction to Statistical Learning with Applications in R by James, G., Witten, D., Hastie, T., and Tibshirani, R.
Econometrics, PhD course at the University of Bonn
Main Textbook: Econometrics by Hayashi, F.
Nonparametric Statistics, Bachelor course at the University of Bonn
Main Textbooks: Nichtparametrische Statistische Methoden by Büning, H., Trenkler, G., and
Local Polynomial Modelling and its Applications by Fan, J. and Gijbels, I.
Computational Statistics, Master course at the University of Bonn
Main Textbooks: Monte Carlo Statistical Methods by Robert, C., Casella, G.,
All of Statistics: A Concise Course in Statistical Inference by Wasserman, L., and
Nonparametric Econometrics: Theory and Practice by Li, Q. and Racine, J. S.

Software

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.