Dominik Liebl
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
2014present: Assistant Professor of Statistics at the Institute for Financial Economics and Statistics, University of Bonn
20132014: Postdoctoral Researcher at the European Center for Advanced Research in Economics and Statistics, Université Libre de Bruxelles
20102013: Research Assistant at the Institute for Econometrics and Statistics, University of Cologne
20082010: 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. 6873

Liebl, D. (2013). Modeling and forecasting electricity prices: A functional data perspective. The Annals of Applied Statistics, 7(3): 15621592

Bada, O. and Liebl, D. (2014). The Rpackage phtt: Panel data analysis with heterogeneous time trends. Journal of Statistical Software, 59(6): 133

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: 104125

Liebl, D., and Kneip, A. (2014). Modelling electricity prices as functional data on random domains. Contributions in InfiniteDimensional 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. 9097

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. 261270
Submitted Working Papers

Liebl, D. (2018). Inference for functional data with covariate adjustments: From sparse to dense and everything inbetween (Resubmitted to: Journal of Multivariate Analysis)

Liebl, D. (2018). Finite sample correction for twosample inference with sparse covariateadjusted functional data (Under preparation for resubmission to: The Annals of Applied Statistics)

Liebl, D., Rameseder, S., and Rust, C. (2018). Functional insights into Google AdWords (Under preparation for resubmission to: The Annals of Applied Statistics)

Kneip, A., and Liebl, D. (2018). On the optimal reconstruction of partially observed functional data (Under preparation for resubmission to: The Annals of Statistics)

Liebl, D., and Rameseder, S. (2018). Partially observed functional data: The case of systematically missing parts (Under preparation for resubmission to: Computational Statistics & Data Analysis)

Liebl, D., and Walders, F. (2018). Parameter regimes in partial functional panel regression (Under preparation for resubmission to: Econometrics and Statistics)

Poß, D., Liebl, D., Eisenbarth, H., Wager, T. D., and Feldman Barrett, L. (2018). (Blinded submission)
Work in Progress

A subsampled penalty criterion to estimate the number of nonvanishing common factors in large panels (with O. Bada)

Take off 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

Observe the Unobservable: Firm Value and FirmSpecific Heterogeneity in Time Trends (with J. Gider)

Lifecycle Wage Trajectories with Missing Data: A Factor Analytic Approach (with P. Pinger)
Teaching Portfolio (Excerpt)

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 coauthor Oualid Bada, I am the creator and maintainer of the Rpackage phtt. The package provides estimation procedures for panel data with general forms of unobservable heterogeneous effects.