Dominik Liebl
I am Assistant Professor of Statistics at the University of Bonn.
My research interests focus on functional data analysis, semi- and nonparametric statistics, and panel data analysis.
Bio
2014-present: Assistant Professor of Statistics at the Institute for Financial Economics and Statistics, University of Bonn
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
2008-2010: PhD Fellow at the Cologne Graduate School
Publications
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Liebl, D., and Walders, F. (2018). Parameter regimes in partial functional panel regression. Econometrics and Statistics. in press. arXiv
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Liebl, D. (2018). Inference for functional data with covariate adjustments. Journal of Multivariate Analysis. in press. arXiv
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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
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Bada, O. and Liebl, D. (2014). The R-package phtt: Panel data analysis with heterogeneous time trends. Journal of Statistical Software, 59(6): 1-33
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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
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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
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Liebl, D. (2013). Modeling and forecasting electricity prices: A functional data perspective. The Annals of Applied Statistics, 7(3): 1562-1592. arXiv
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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
Submitted Working Papers
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Liebl, D., and Rameseder, S. (2018). Partially observed functional data: The case of systematically missing parts (Resubmitted to Computational Statistics & Data Analysis)
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Liebl, D. (2018). Nonparametric testing for differences in electricity prices: The case of the Fukushima nuclear accident (Resubmitted to The Annals of Applied Statistics)
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Kneip, A., and Liebl, D. (2018). On the optimal reconstruction of partially observed functional data (Under preparation for resubmission to The Annals of Statistics)
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Poß, D., Liebl, D., Eisenbarth, H., Wager, T. D., and Feldman Barrett, L. (2018). (Blinded submission to the Journal of the American Statistical Association)
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Liebl, D., Rameseder, S., and Rust, C. (2018). Improving estimation in functional linear regression with points of impact: Insights into Google AdWords (Under review at the Journal of Computational and Graphical Statistics)
Work in Progress
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A subsampled penalty criterion to estimate the number of non-vanishing common factors in large panels (with O. Bada)
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Take off your shoes! Analyzing the foot strike behavior of habitual shod runners when running barefoot (with S. Willwacher, J. Hamill, and G. P. Brüggemann)
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On the minor role of bandwidth selection when smoothing sparse to dense functional data
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Observe the Unobservable: Firm Value and Firm-Specific Heterogeneity in Time Trends (with J. Gider)
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Life-cycle Wage Trajectories with Missing Data: A Factor Analytic Approach (with P. Pinger)
Teaching Portfolio (Excerpt)
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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.
Software
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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.
Institutions & Affiliations
