The statistics and data science group focuses on application-driven methodological research, particularly statistical learning for categorical, functional, and high-dimensional data. We also offer consulting services on statistics and data analytics to collaborators in economics, social sciences, and life sciences.
Research interests:
Functional, categorical, and high-dimensional data
Statistical and machine learning
Structural Health Monitoring
Selected Research Projects:
DFG Research Grant: Statistical Methods and Models for Interdependent Categorical, particularly Ordinal Data
There are various statistical methods available for analyzing and modeling high-dimensional, interdependent variables, such as graphical models or principal component analysis. Those methods, however, usually require continuous or metrically scaled data. Corresponding methods for categorical, particularly ordinal data are rather limited, although this kind of data is frequently found in various applications. Therefore, the goal of the project is to fill this gap in statistical methodology by developing appropriate methods, such as regularized graphical models and principal component analysis for ordinal variables
Research Group: Prof. Dr. Jan Gertheiss; Aisouda Hoshiyar, M.Sc.; Ejike Richard Ugba, M.Sc.
Funding Period: 2019 – 2022
Subproject Data Analytics, Joint dtec.bw Research Project SHM – Digitization and Monitoring of Bridge Infrastructure
Within the joint research project Structural Health Monitoring (SHM) we aim at assessing existing and potentially damaged highway bridges by means of different monitoring systems in an integrated, digital framework (details).
In our subproject Data Analytics we investigate spatio-temporal associations within and between sensor streams and develop/adapt machine learning methods for feature extraction and damage detection.
Research Group: Prof. Dr. Jan Gertheiss, Lizzie Neumann, M.Sc.; Frederike Vogel, M.Sc.; Dr. Philipp Wittenberg
Funding Period: 2021 – 2026
The project HPC for semi-parametric statistical modeling on massive datasets is an important addition and extension for the dtec.bw project SHM – Digitization and Monitoring of Bridge Infrastructure. Given the enormous size of the datasets (several years of high-resolution sensor data), we are excited to collaborate with the hpc.bw team on the HSUper cluster.
The project’s main goal is to efficiently implement estimation of semi-parametric and non-parametric models for structural change monitoring and detection.
This collaboration improves the efficiency and scalability of data analytic modeling processes, contributing to the broader field of infrastructure monitoring.
Research Group: Dr. Philipp Wittenberg, Lizzie Neumann, M.Sc.
Funding Period: 2023 – 2024
Recent publications:
Gertheiss, J. and A. Groll (2025). Penalisierte Regression. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007/978-3-662-63496-7_12-1
Gertheiss, J., L. Neumann, and P. Wittenberg (2025). Bridge Health Monitoring Under Varying Environmental Conditions Using Conditional Principal Component Analysis. In: Cunha, Á., Caetano, E. (eds) Experimental Vibration Analysis for Civil Engineering Structures. EVACES 2025. Lecture Notes in Civil Engineering, vol 675. Springer, Cham. doi: 10.1007/978-3-031-96106-9_43
Neumann, L., P. Wittenberg, A. Mendler, and J. Gertheiss (2025). Confounder-adjusted covariances of system outputs and applications to structural health monitoring. Mechanical Systems and Signal Processing 224, 111083, doi: 10.1016/j.ymssp.2024.111983
Neumann, L., P. Wittenberg, and J. Gertheiss (2025). Confidence Intervals for Conditional Covariances of Natural Frequencies. In: Proceedings of the IOMAC 2025 (to appear)
Neumann, L. (2025). Monitoring Confounder-adjusted Principal Component Scores with an Application to Load Test Data. In: Proceedings of the 35th European Safety and Reliability & the 33rd Society for Risk Analysis Europe Conference. Edited by Eirik Bjorheim Abrahamsen, Terje Aven, Frederic Bouder, Roger Flage, Marja Ylönen. Research Publishing, Singapore, 2985-2992, doi:10.3850/978-981-94-3281-3-procd
Siebenmorgen, C., M.S. Grønbeck, A. Schubert, J. Gertheiss, and J. Mörlein (2025). Updating descriptive sensory evaluation of chicken: proposing new protocols and statistical analysis. Poultry Science, 104(11), 105807, doi: 10.1016/j.psj.2025.105807
Tu, D., J. Wrobel, T.D. Satterthwaite, J. Goldsmith, R.C. Gur, R.E. Gur, J. Gertheiss, D.S. Bassett, and R.T. Shinohara (2025). Regression and alignment for functional data and network topology. Biostatistics, 26(1), kxae026, doi: 10.1093/biostatistics/kxae026
Wittenberg P., A. Mendler, S. Knoth, and J. Gertheiss (2025). Multivariate Long-Term Profile Monitoring with Application to the KW51 Railway Bridge. In: Cunha, Á., Caetano, E. (eds) Experimental Vibration Analysis for Civil Engineering Structures. EVACES 2025. Lecture Notes in Civil Engineering, vol 676. Springer, Cham. doi: 10.1007/978-3-031-96114-4_48
Wittenberg P., L. Neumann L., A. Mendler, and J. Gertheiss (2025). Covariate-adjusted functional data analysis for structural health monitoring. Data-Centric Engineering, 6:e27, doi: 10.1017/dce.2025.18
Gertheiss, J., D. Rügamer, and S. Greven (2024). Methoden für die Analyse funktionaler Daten. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007/978-3-662-63496-7_5-1
Gertheiss, J., D. Rügamer, B.X.W. Liew, and S. Greven (2024). Functional Data Analysis: An Introduction and Recent Developments. Biometrical Journal, 66: e202300363, doi: 10.1002/bimj.202300363
Vogel, F.. (2024). Examining Quantiles in Structural Health Monitoring. In: Proceedings of the 10th European Workshop on Structural Health Monitoring (EWSHM 2024), e-Journal of Nondestructive Testing, doi: 10.58286/29664
Windmann, A., Wittenberg, P., Schieseck, M. and Niggemann, O. (2024). Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems. In: 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), Beijing, China, 2024, pp. 1-8 doi: 10.1109/INDIN58382.2024.10774364
Gertheiss, J. and R.T. Shinohara (2023). Penalized non-linear canonical correlation analysis for ordinal data with application to the international classification of functioning, disability and health. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 532 – 540, doi: 10.1137/1.9781611977653.ch60
Gertheiss, J. and G. Tutz (2023). Generalisierte lineare und gemischte Modelle. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007/978-3-662-63496-7_1-1
Gertheiss, J. and G. Tutz (2023). Regularization and Predictor Selection for Ordinal and Categorical Data. In: Kateri, M., Moustaki, I. (eds) Trends and Challenges in Categorical Data Analysis. Statistics for Social and Behavioral Sciences. Springer, Cham, 199-232, doi: 10.1007/978-3-031-31186-4_7
Hesselmann, C., D. Reinhardt, J. Gertheiss, and J.P. Müller (2023). Data privacy in ride-sharing services: From an analysis of common practices to improvement of user awareness. In Reiser, H.P., Kyas, M. (eds.) Secure IT Systems, NordSec 2022, Lecture Notes in Computer Sciences. Springer, Cham, 20-39, doi: 10.1007/978-3-031-22295-5_2
Hoshiyar, A., H.A.L. Kiers, and J. Gertheiss (2023). Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets. British Journal of Mathematical and Statistical Psychology 76(2), 353-371, doi: 10.1111/bmsp.12297
M.C. Morais, P. Wittenberg and S. Knoth (2023). An ARL-unbiased modified chart for monitoring autoregressive counts with geometric marginal distributions. Sequential Analysis 42(3), 323-347, doi: 10.1080/07474946.2023.2221996
M.C. Morais, P. Wittenberg and C.J. Cruz (2023). An ARL-Unbiased Modified np-Chart for Autoregressive Binomial Count. Stochastics and Quality Control 38(1), 11-24, doi: 10.1515/eqc-2022-0052
Neumann, L. (2023). Covariate-adjusted Association of Sensor Outputs using a Nonparametric Estimate of the Conditional Covariance. In: Proceedings of the 37th International Workshop on Statistical Modelling: Volume I., Dortmund, Germany, 543-548.
Selk, L. and J. Gertheiss (2023). Nonparametric regression and classification with functional, categorical, and mixed covariates. Advances in Data Analysis and Classification 17(2), 519-543, doi: 10.1007/s11634-022-00513-7
Ugba, E.R. and J. Gertheiss (2023). A modification of McFadden’s R2 for binary and ordinal response models. Communications for Statistical Applications and Methods 30(1), doi: 10.29220/CSAM.2023.30.1.049
Wittenberg, P. and J. Gertheiss (2023). Modelling SHM sensor outputs: A functional data approach. Proceedings of the 37th International Workshop on Statistical Modelling, Vol. I, 664-668
