Dominik Liebl
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, ecommerce (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 Rpackages at CRAN and GitHub accompanying my research papers.
Bio

2014present: Assistant Professor of Statistics at the Institute for Financial Economics and Statistics, University of Bonn

2019March: Research Visit at the UC Berkeley Simons Institute for the Theory of Computing

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

2010: Research Visit at the Working Group STAPH, Université ToulouseIIIPaulSabathier

20082010: PhD Fellow at the Cologne Graduate School
Publications

Liebl, D. (2019). Nonparametric testing for differences in electricity prices: The case of the Fukushima nuclear accident. The Annals of Applied Statistics (accepted). Supplementary Paper RCodes & Data

Liebl, D., and Rameseder, S. (2019). Partially observed functional data: The case of systematically missing parts. Computational Statistics & Data Analysis, 131, 104115 arXiv Rpackage

Liebl, D., and Walders, F. (2019). Parameter regimes in partial functional panel regression. Econometrics and Statistics. in press. arXiv Supplementary Paper

Liebl, D. (2019). Inference for sparse and dense functional data with covariate adjustments. Journal of Multivariate Analysis, 170, 315335 arXiv

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 (in press).

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

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

Liebl, D., and Kneip, A. (2014). Modelling electricity prices as functional data on random domains. Contributions in InfiniteDimensional Statistics and Related Topics. Edited by: Bongiorno, E. G., Salinelli, E., Goia, A., Vieu, P., pp. 9097

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 preprint

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

Liebl, D. (2013). Contributions to functional data analysis with applications to modeling time series and panel data. PhDThesis  University of Cologne

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
Submitted Working Papers

Kneip, A., and Liebl, D. (2019). On the optimal reconstruction of partially observed functional data (Resubmitted to The Annals of Statistics) Rpackage

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)
Work in Progress

Superconsistent estimation of points of impact in nonparametric regression with functional predictors (with Poß, D., Kneip, A., Eisenbarth, H., Wager, T. D., and Feldman Barrett, L.) preprint (old version)

Take off your shoes! Analyzing the foot strike behavior of habitual shod runners when running barefoot (with Willwacher, S., Hamill, J., and Brüggemann, G. P.)

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

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 Gider, J.)

Lifecycle wage trajectories with missing data: A factor analytic approach (with Pinger, P.)
Recent & Upcoming Talks

Workshop on Functional Data Analysis and Beyond, Dec. 314, 2018, Creswick Melbourne, Australia (invited by Aurore Delaigle, Debashis Paul, and Frédéric Ferraty).

11th International Conference on Computational and Methodological Statistics (CMStatistics 2018), Dec. 1416, 2018, University of Pisa, Italy (invited by Gil González Rodríguez)

CRoNoS & MDA 2019, 2nd Workshop on Multivariate Data and Software, Apr. 1416, 2019, Limassol, Cyprus (invited by Alessandra Menafoglio)

3rd International Workshop on Advances in Functional Data Analysis, May 2324, 2019, Castro Urdiales, Spain (invited by Paula Navarro)

European Meeting of Statisticians (EMS 2019), Jul. 2226, 2019, Palermo, Italy (invited by Laura Sangalli)

10th International Workshop on Simulation and Statistics, Sept. 26, 2019, Salzburg, Austria (invited by Siegfried Hörmann)

12th International Conference on Computational and Methodological Statistics (CMStatistics 2019), Dec. 1416, 2019, London, UK (invited by Alexander Aue)

5th International Workshop on Functional and Operatorial Statistics (IWFOS 2020), Jun. 2427, 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
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 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.

Rpackage FunRegPoI

Rpackage PartiallyFD

Rpackage ReconstPoFD

Rpackage GFLMPOI (under revision)