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, for instance, in empirical economics, energy economics, finance, ecommerce (e.g., Google AdWords data), and psychology. 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 of Finance 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
Publications in Statistics

Liebl, D., Rameseder, S., and Rust, C. (2020). Improving estimation in functional linear regression with points of impact: Insights into Google AdWords. Journal of Computational and Graphical Statistics (accepted) Rpackage

Kneip, A., and Liebl, D. (2020). On the optimal reconstruction of partially observed functional data. The Annals of Statistics (forthcoming) Rpackage

Liebl, D. (2019). Nonparametric testing for differences in electricity prices: The case of the Fukushima nuclear accident. The Annals of Applied Statistics, 13(2), 11281146 Supplementary Paper RCodes & Data arXiv

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, 11, 105115 arXiv Supplementary Paper

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

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

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

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., 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
Further Publications

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, 47(8), 19751983

Pataky, T. C., Vanrenterghem, J., Robinson, M. A., Liebl, D. (2019). On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth onedimensional biomechanical data. Journal of Biomechanics, 91, 114123

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), 26992706.

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
PhD Thesis

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

Liebl, D. and Reimherr M. (2019). Fast and fair simultaneous confidence bands for functional parameters (Under review at the Journal of the Royal Statistical Society, Series B. Status: Major Revision) Rpackage

Bada, O., Kneip, A., Liebl, D., 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). Superconsistent estimation of points of impact in nonparametric regression with functional predictors (Under review at the Journal of the Royal Statistical Society, Series B. Status: Major Revision) Rpackage
Work in Progress

Bada, O. and Liebl, D. A subsampled penalty criterion to estimate the number of nonvanishing 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 firmspecific 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 MCARassumption for partially observed functional data
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 Royal Statistical Society: Series B (JRSSB), Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS), Journal of Time Series Analysis (JTSA), Computational Statistics & Data Analysis (CSDA), Statistical Modeling: An International Journal (SMIJ), Advances in Statistical Analysis (AStA), 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)
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 fdapoi

Rpackage ffscb