Dominik Liebl
Welcome
… to my website. I am an Associate Professor of Statistics at the University of Bonn.
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 (energy) economics, finance, ecommerce (e.g., Google AdWords data), emotion psychology, biomechanics, and human movement sciences.
My current methodological and theoretical research interests focus on functional data analysis, nonparametric statistics, longitudinal data analysis, simultaneous inference, and statistical fairness. Last but not least, I am committed to providing the computational implementations of my statistical research and publish Rpackages at CRAN and GitHub accompanying my research papers.
Bio

2020present: Associate Professor of Statistics at the Institute of Finance and Statistics, University of Bonn

20142020: 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

2010: Research Visit, Working Group STAPH, Université ToulouseIIIPaulSabathier

20082010: PhD at the Institute for Econometrics and Statistics, University of Cologne
Publications
Publications in Statistics

Liebl, D. and Reimherr M. (2023). Fast and fair simultaneous confidence bands for functional parameters. Journal of the Royal Statistical Society, Series B (accepted) PDF Rpackage

Pataky, T.C., Abramowicz, K., Liebl, D., Pini, A., Sjöstedt de Luna, S., and Schelin, L. (2023). Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with intervalwise testing. AStA Advances in Statistical Analysis, 107, 369392 Replication Codes

Bada, O., Kneip, A., Liebl, D., Mensinger, T., Gualtieri, J., and Sickles R. C. (2022). A wavelet method for panel models with jump discontinuities in the parameters. Journal of Econometrics, 226(2), 399422 PDF Supplementary Paper Rpackage Replication Codes

Poß, D., Liebl, D., Kneip, A., Eisenbarth, H., Wager, T. D. and Feldman Barrett, L. (2020). Superconsistent estimation of points of impact in nonparametric regression with functional predictors. Journal of the Royal Statistical Society, Series B, 82(4), 11151140 PDF Rpackage

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, 29(4), 814826 PDF Rpackage

Kneip, A. and Liebl, D. (2020). On the optimal reconstruction of partially observed functional data. The Annals of Statistics, 48(3), 16921717 PDF 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 PDF 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 PDF Rpackage

Liebl, D. and Walders, F. (2019). Parameter regimes in partial functional panel regression. Econometrics and Statistics, 11, 105115 PDF Supplementary Paper

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

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 PDF RCodes & Data
Book Chapters & Editorials

Groll, A. and Liebl, D. (2023). Editorial special issue: Statistics in sports. AStA Advances in Statistical Analysis, 107, 17

Liebl, D. and Reimherr, M. (2020). Simultaneous Inference for Functionvalued Parameters: a Fast and Fair Approach. Functional and HighDimensional Statistics and Related Fields. Edited by: Aneiros, G., Horová, I., Hužková, M. and Vieu, P., pp. 153159

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

Richter, E., Liebl, D., Schulte, B., Lehmann, N., Fuhrmann, C., Jöckel, KH., Ioannidis, J. P. A. and Streeck, H. (2023). Analysis of fatality impact and seroprevalence surveys in a community sustaining a SARSCoV2 superspreading event. Scientific Reports, 13, 5440

Liebl, D. (2022). Letter to the Editor on: "Comparing Groups of Time Dependent Data Using Locally Weighted Scatterplot Smoothing AlphaAdjusted Serial Ttests" by Niiler (2020) Gait & Posture, 92, 477479 preprint

Warmenhoven, J., Bargary, N., Liebl, D., Harrison, A. J., Robinson, M. A., Gunning, E. and Hooker, G. (2021). PCA of Waveforms and Functional PCA: A Primer for Biomechanics. Journal of Biomechanics, 116, 110106

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. and 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
Open Reviews
Working Papers and Work in Progress

Liebl, D. and Schüwer U. (2022). ProTrump partisanship and COVID19 mortality: A modelbased counterfactual analysis SSRN

Creutzinger m., Liebl, D., and Sharp, J. Fair prediction bands for function on scalar regression models

Liebl, D. and Mensinger, T. Fair causal inference with functional data

Kneip A., Liebl, D., and Otto, S. Functional linear regression with concurrent point of impact

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

Liebl, D. and Rust, C. Directed local testing in the functional linear model
Editorial Services

Associate Editor (since 2021), Journal of the Royal Statistical Society: Series C

Guest Editor (20202022), Special Issue: Statistics in Sports, AStAAdvances in Statistical Analysis (with Andreas Groll, TU Dortmund University)
Refereeing
The Annals of Statistics (AOS), Journal of the Royal Statistical Society: Series B (JRSSB), Journal of the American Statistical Association (JASA), Biometrika, The Annals of Applied Statistics (AOAS), Journal of the Royal Statistical Society: Series C (JRSSC), Journal of Time Series Analysis (JTSA), Computational Statistics & Data Analysis (CSDA), Statistical Modeling: An International Journal (SMIJ), Advances in Statistical Analysis (AStA), Econometrics and Statistics (ECOSTA), 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), TEST: Journal of the Spanish Society of Statistics and Operations Research, Journal of Statistical Planning and Inference (JSPI), Journal of Multivariate Analysis (JMVA), Statistica Sinica (SS), Statistics and Probability Letters (STAPRO)
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 sawr

Rpackage ffscb
Teaching Portfolio
Excerpt of past and current courses:

Computational Statistics/Data Science (M.Sc., B.Sc., and PhD) at the University Bonn
Main Textbook: Introduction to Statistical Learning. Contents: Statistical Learning, Regression Analysis, Classification, Resampling Methods, Linear Model Selection and Regularization, Multiple testing, TreeBased Methods, Functional Data Analysis, Fairness 
The beginnings of a lecture script for Computational Statistics (fun side project in German): Online Skrip (German)

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 
Econometrics (M.Sc. and PhD) at the University of Bonn. Contents: Probability Theory, Test Theory, Multiple Linear Regression, Small and Large Sample Inference, Maximum Likelihood Inference, Instrumental Variable Estimation, Panel Data Analysis, Time Series Analysis, Causal Inference

Nonparametric Statistics (B.Sc.) 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. Contents: Test Theory, Linear Rank Statistics, Introduction to Mathematical Statistics, Kernel Density Estimation, Nonparametric Regression Analysis
Fundings & Affiliations

My research is generously funded by the Hausdorff Center of Mathematics (HCM), a Cluster of Excellence at the University of Bonn, funded by the German Science Foundation (DFG).

I am member of the Transdisciplinary Research Area (TRA) Mathematics, Modelling and Simulation of Complex Systems at the University of Bonn.

I am Visiting Associate at the Department of Statistics of the Colorado State University.

I am member of the management committee of the COST Action CA21163  Text, functional and other highdimensional data in econometrics: New models, methods, applications, funded by the European Union.