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, e-commerce (e.g., Google AdWords data), and psychology. 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 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

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

• 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 arXiv R-Codes & 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. 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

• 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., 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

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.) R-package

• Liebl, D., Rameseder, S., and Rust, C. (2019). Improving estimation in functional linear regression with points of impact: Insights into Google AdWords (Under review at the Journal of Computational and Graphical Statistics. Status: Major Revision) R-package

• 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). Super-consistent 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) R-package

Work in Progress

• 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

• Gider, J. and Liebl, D. Observe the unobservable: Firm value and firm-specific 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 MCAR-assumption for partially observed functional data

Recent & Upcoming Talks

• 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)

• Seminar Talk, Umeå University, Sweden, Oct. 30

• Seminar Talk, University College Dublin, Ireland, Nov. 7

• Seminar Talk, University Carlos III Madrid, Spain, Nov. 25

• 12th International Conference on Computational and Methodological Statistics (CMStatistics 2019), Dec. 14-16, 2019, London, UK (invited by Alexander Aue)

• Seminar Talk, Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt-University Berlin, Germany, Jan. 8, 2020 (invited by Sonja Greven)

• 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 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 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 ffscb

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Institutions & Affiliations

Miscellaneous

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