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, e-commerce (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 R-packages at CRAN and GitHub accompanying my research papers.

Bio

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

• 2014-2020: Assistant Professor of Statistics at the Institute of Finance 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

Publications in Statistics

• 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), 399-422 arXiv Supplementary Paper R-package Replication Codes

• Groll, A. and Liebl, D. (2022). Editorial special issue: Statistics in sports. Forthcoming at AStA Advances in Statistical Analysis

• Pataky, T.C., Abramowicz, K., Liebl, D., Pini, A., Sjöstedt de Luna, S., and Schelin, L. (2021). Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with interval-wise testing. Forthcoming at AStA Advances in Statistical Analysis Replication Codes

• Poß, D., Liebl, D., Kneip, A., Eisenbarth, H., Wager, T. D. and Feldman Barrett, L. (2020). Super-consistent estimation of points of impact in nonparametric regression with functional predictors. Journal of the Royal Statistical Society, Series B, 82(4), 1115-1140 R-package arXiv

• 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), 814-826 R-package arXiv

• Kneip, A. and Liebl, D. (2020). On the optimal reconstruction of partially observed functional data. The Annals of Statistics, 48(3), 1692-1717 R-package arXiv

• 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 R-package arXiv

• Liebl, D. and Walders, F. (2019). Parameter regimes in partial functional panel regression. Econometrics and Statistics, 11, 105-115 Supplementary Paper arXiv

• Liebl, D. (2019). Inference for sparse and dense functional data with covariate adjustments. Journal of Multivariate Analysis, 170, 315-335 arXiv

• 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. (2013). Modeling and forecasting electricity prices: A functional data perspective. The Annals of Applied Statistics, 7(3), 1562-1592 R-Codes & Data arXiv

Book Chapters

• Liebl, D. and Reimherr, M. (2020). Simultaneous Inference for Function-valued Parameters: a Fast and Fair Approach. Functional and High-Dimensional Statistics and Related Fields. Edited by: Aneiros, G., Horová, I., Hužková, M. and Vieu, P., pp. 153-159

• 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

• 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., 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

Further Publications

• Liebl, D. (2022). Letter to the Editor on: "Comparing Groups of Time Dependent Data Using Locally Weighted Scatterplot Smoothing Alpha-Adjusted Serial T-tests" by Niiler (2020) Gait & Posture, 92, 477-479 preprint

• Warmenhoven, J., Bargary, N., Liebl, D., Harrison, A. J., Robinson, M. A., Gunning, E., 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), 1975-1983

• Pataky, T. C., Vanrenterghem, J., Robinson, M. A. and Liebl, D. (2019). On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data. Journal of Biomechanics, 91, 114-123

• 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

PhD Thesis

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

Open Reviews

• Liebl, D. (2020). Open Review of the Manuscript: An Analysis of Sars-Cov-2 Viral Load by Patient Age by Terry C. Jones, Barbara Mühlemann, Talitha Veith, Marta Zuchowski​, Jörg Hofmann, Angela Stein​, Anke Edelmann​, Victor Max Corman​, and Christian Drosten.

Working Papers

• Liebl, D. and Reimherr M. (2022). Fast and fair simultaneous confidence bands for functional parameters (Under review at the Journal of the Royal Statistical Society, Series B. Status: Major Revision) R-package

• Liebl, D. and Schüwer U. (2022). Pro-Trump partisanship and COVID-19 mortality: A model-based counterfactual analysis SSRN

Work in Progress

• 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 non-vanishing 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

• Gider, J. and Liebl, D. Observe the unobservable: Firm value and firm-specific heterogeneity in time trends

• Hörmann, S., Kraus, D., and Liebl, D. Testing the MCAR-assumption for partially observed functional data

Editorial Services

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

• Guest Editor (2020-2022), Special Issue: Statistics in Sports, AStA-Advances 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 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

• R-package sawr

• R-package 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, Tree-Based 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

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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 high-dimensional data in econometrics: New models, methods, applications, funded by the European Union.
  

  
  

Miscellaneous

• My former life took (mostly) place in dojos, where I used to wear a judogi.