Welcome
… to my website. I am an Assistant 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 in empirical economics, energy economics, finance, e-commerce (e.g., Google AdWords data), and bioscience (e.g., human movement data). I am committed to providing the computational implementations of my statistical research and publish R-packages at CRAN and GitHub accompanying my research papers.

Bio

2014-present: Assistant Professor of Statistics at the Institute for Financial Economics and Statistics, University of Bonn
2019-March: Research Visit at the UC Berkeley Simons Institute for the Theory of Computing
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
2010: Research Visit at the Working Group STAPH, Université Toulouse-III-Paul-Sabathier
2008-2010: PhD Fellow at the Cologne Graduate School

Publications

Statistical Methods

Kneip, A., and Liebl, D. (2019). On the optimal reconstruction of partially observed functional data. The Annals of Statistics (accepted). R-package
Liebl, D. (2019). Nonparametric testing for differences in electricity prices: The case of the Fukushima nuclear accident. The Annals of Applied Statistics 13(2), 1128-1146 Supplementary Paper R-Codes & Data arXiv
Liebl, D., and Rameseder, S. (2019). Partially observed functional data: The case of systematically missing parts. Computational Statistics & Data Analysis, 131, 104-115 arXiv R-package
Liebl, D., and Walders, F. (2019). Parameter regimes in partial functional panel regression. Econometrics and Statistics 11, 105-115 arXiv Supplementary Paper
Liebl, D. (2019). Inference for sparse and dense functional data with covariate adjustments. Journal of Multivariate Analysis, 170, 315-335 arXiv
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
Bada, O. and Liebl, D. (2014). phtt: Panel data analysis with heterogeneous time trends in R. Journal of Statistical Software, 59(6), 1-33 R-package
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. (2013). Modeling and forecasting electricity prices: A functional data perspective. The Annals of Applied Statistics, 7(3), 1562-1592 arXiv R-Codes & Data
Liebl, D. (2013). Contributions to functional data analysis with applications to modeling time series and panel data. PhD-Thesis - University of Cologne

Statistical Applications

Hollander, K., Liebl, D., Willwacher, S., Meining, S., Mattes, K. and Zech, A. (2019). Adaptation of running biomechanics to repeated barefoot running: A randomized controlled study. The American Journal of Sports Medicine (in press)
Pataky, T. C., Vanrenterghem, J., Robinson, M. A., Liebl, D. (2019). On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data. Journal of Biomechanics (in press)
Hamacher, D., Liebl, D., Hödl, C., Heßler, V., Kniewasser, C. K., Thönnessen, T. and Zech, A. (2019). Gait stability and its influencing factors in older adults. Frontiers in Physiology, 9.
Zech, A., Meining, S., Hötting, K., Liebl, D., Mattes, K. and Hollander, K. (2018). Effects of barefoot and footwear conditions on learning of a dynamic balance task: A randomized controlled study. European Journal of Applied Physiology, 118(12), 2699-2706.
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 preprint
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

Submitted Working Papers

Liebl, D., Rameseder, S., and Rust, C. (2019). Improving estimation in functional linear regression with points of impact: Insights into Google AdWords (Resubmitted to the Journal of Computational and Graphical Statistics)
Bada, O., Liebl, D., Kneip, A., Gualtieri, J., and Sickles R. C. (2019). A wavelet method for panel models with jump discontinuities in the parameters (Under review at the Journal of Econometrics. status: revise and resubmit)
Poß, D., Liebl, D., Kneip, A., Eisenbarth, H., Wager, T. D., and Feldman Barrett, L. (2019). Super-consistent estimation of points of impact in nonparametric regression with functional predictors (Submitted to the Journal of the Royal Statistical Society, Series B) arXiv R-package

Work in Progress

Liebl. D. and Reimherr M. Fast and Fair Simultaneous Confidence Bands
Liebl, D., Willwacher, S., Hamill, J. and Brüggemann, G. P. Take off your shoes! Analyzing the foot strike behavior of habitual shod runners when running barefoot
Bada, O. Liebl, D. A subsampled penalty criterion to estimate the number of non-vanishing common factors in large panels
Liebl, D. 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 Pinger, P. Life-cycle wage trajectories with missing data: A factor analytic approach

Recent & Upcoming Talks

Workshop on Functional Data Analysis and Beyond, Dec. 3-14, 2018, Creswick Melbourne, Australia (invited by Aurore Delaigle, Debashis Paul, and Frédéric Ferraty).
11th International Conference on Computational and Methodological Statistics (CMStatistics 2018), Dec. 14-16, 2018, University of Pisa, Italy (invited by Gil González Rodríguez)
CRoNoS & MDA 2019, 2nd Workshop on Multivariate Data and Software, Apr. 14-16, 2019, Limassol, Cyprus (invited by Alessandra Menafoglio)
3rd International Workshop on Advances in Functional Data Analysis, May 23-24, 2019, Castro Urdiales, Spain (invited by Paula Navarro)
Workshop on New Developments in Econometrics and Time Series, June 6-7, 2019, Graz, Austria (invited by H. Dette, M. Hallin and S. Hörmann)
European Meeting of Statisticians (EMS 2019), Jul. 22-26, 2019, Palermo, Italy (invited by Laura Sangalli)
10th International Workshop on Simulation and Statistics, Sept. 2-6, 2019, Salzburg, Austria (invited by Siegfried Hörmann)
12th International Conference on Computational and Methodological Statistics (CMStatistics 2019), Dec. 14-16, 2019, London, UK (invited by Alexander Aue)
5th International Workshop on Functional and Operatorial Statistics (IWFOS 2020), Jun. 24-27, 2020, Brno, Czech Republic (invited by Philippe Vieu, Germán Aneiros, Ivana Horová, and Marie Hušková)

Teaching Portfolio

Excerpt of past and current courses:

Research Module in Econometrics & Statistics, Master Course at the University Bonn
Course Material
Short lecture A Gentle Introduction to Functional Data Analysis at the University of Passau
Main Textbook: Introduction to Functional Data Analysis by Kokoszka, P., and Reimherr, M.
Course Material
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.

Refereeing

Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS), Journal of Time Series Analysis (JTSA), Computational Statistics & Data Analysis (CSDA), Journal of the Royal Statistical Society: Series B (JRSSB), Statistical Modeling: An International Journal (SMIJ), Advances in Statistical Analysis (AStA), Journal of Sport and Health Science (JSHS), Journal of Applied Statistics (JAS), Energy Economics (ENEECO), Journal of Statistical Planning and Inference (JSPI), Journal of Multivariate Analysis (JMVA), Statistica Sinica (SS)

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.
R-package FunRegPoI
R-package PartiallyFD
R-package ReconstPoFD
R-package fdapoi