On the one hand, the statistics and data science group is focusing on application-driven methodological research, particularly regularization and ensemble methods for categorical, functional and high-dimensional data. On the other hand, we are offering consulting services on statistics and data analytics to collaborators from, e.g., economics, social 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:
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
Gertheiss, J., D. Rügamer, B.X.W. Liew, and S. Greven (2024). Functional Data Analysis: An Introduction and Recent Developments. Biometrical Journal, to appear, doi: 10.1002/bimj.202300363
Tu, D., J. Wrobel, T.D. Satterthwaite, J. Goldsmith, R.C. Gur, R.E. Gur, J. Gertheiss, D.S. Bassett, and R.T. Shinohara (2024). Regression and alignment for functional data and network topology. Biostatistics, to appear, doi: 10.1093/biostatistics/kxae026
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
Gertheiss, J., D. Rügamer, and S. Greven (2023). Methoden für die Analyse funktionaler Daten. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik. Springer, to appear
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, to appear
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
Altmann, B. A., J. Gertheiss, I. Tomasevic, C. Engelkes, T. Glaesener, J. Meyer, A. Schäfer, R. Wiesen, and D. Mörlein (2022). Human perception of color differences using computer vision system measurements of raw pork loin. Meat Science 188, 108766, doi: 10.1016/j.meatsci.2022.108766
Gertheiss, J. and F. Jaehn (2022). Route planning under uncertainty: A case study apart from mean travel time. In Trautmann, N, Gnädi, M. (eds.) Operations Research Proceedings 2021, OR 2021, Lecture Notes in Operations Research. Springer, Cham, 261-267, doi: 10.1007/978-3-031-08623-6_39
Gertheiss, J., F. Scheipl, T. Lauer, and H. Ehrhardt (2022). Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia. BMC Research Notes 15(1), 112, doi: 10.1186/s13104-022-05995-4
Morais, M.C., P. Wittenberg, and C.J. Cruz (2022). The np-chart with 3-sigma limits and the ARL-unbiased np-chart revisited. Stochastics and Quality Control 37(2), 107-116, doi: 10.1515/eqc-2022-0032
Neumann, L. and J. Gertheiss (2022). Covariate-adjusted Association of Sensor Outputs for Structural Health Monitoring. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1, 287-291, doi: 10.24405/14566
Selk, L. and J. Gertheiss (2022). Nonparametric regression and classification with functional, categorical, and mixed covariates. Advances in Data Analysis and Classification, doi: 10.1007/s11634-022-00513-7
Ugba, E.R. (2022). gofcat: An R package for goodness-of-fit of categorical response models. Journal of Open Source Software 7(76), 4382, doi: 10.21105/joss.04382
Vogel, F., N.M. Vahle, J. Gertheiss, and M.J. Tomasik (2022). Supervised learning for analysing movement patterns in a virtual reality experiment. Royal Society Open Science 9(4), 211594, doi: 10.1098/rsos.211594
Weiß, C.H., F. Zhu, and A. Hoshiyar (2022). Softplus INGARCH Models. Statistica Sinica 32, 1099-1120, doi: 10.5705/ss.202020.0353
Wittenberg, P., M.C. Morais, and W.H. Woodall (2022). Comments on “On scale parameter monitoring of the Rayleigh distributed data using a new design”. IEEE Access 10, 84622-84625, doi: 10.1109/ACCESS.2022.3196664
Hesselmann, C., J. Gertheiss, and J.P. Müller (2021). Ride sharing & data privacy: How data handling affects the willingness to disclose personal information. Findings, November, doi: 10.32866/001c.29863
Hoshiyar, A. (2021). ordPens: An R package for Selection, Smoothing and Principal Components Analysis for Ordinal Variables. Journal of Open Source Software 6(68), 3828, doi: 10.21105/joss.03828
Rohweder, N.O., J. Gertheiss, and C. Rembe (2021). Sub-micron pupillometry for optical EEG measurements. tm-Technisches Messen 88 (7-8), 473-480, doi: 10.1515/teme-2021-0030
Ugba, E.R. (2021). serp: An R package for smoothing in ordinal regression. Journal of Open Source Software 6(66), 3705, doi: 10.21105/joss.03705
Ugba, E.R., D. Mörlein, and J. Gertheiss (2021). Smoothing in ordinal regression: An application to sensory data. Stats 4 (3), 616-633, doi: 10.3390/stats4030037
Aipperspach, C., J. Gertheiss, and C. Jahn (2020). CO2-Ausstoß auf See: Sind genauere Schätzungen möglich? Potentiale eines Stichproben-basierten Modells. Internationales Verkehrswesen 72 (3), 65-71
Alhaji, B., J. Beecken, R. Ehlers, J. Gertheiss, F. Merz, J. Müller, M. Prilla , A. Rausch, A. Reinhardt, D. Reinhardt, C. Rembe, N.-O. Rohweder, C. Schwindt, S. Westphal, and J. Zimmermann (2020). Engineering human-machine teams for trusted collaboration. Big Data and Cognitive Computing 4, 35, doi: 10.3390/bdcc4040035
Hoshiyar, A. (2020). Analyzing Likert-type data using penalized non-linear principal components analysis. In: Proceedings of the 35th International Workshop on Statistical Modelling, Vol. I, 337-340
Lauer, T., J. Behnke, F. Oehmke, J. Bäcker, K. Gentil, T. Chakraborty, M. Schloter, J. Gertheiss, and H. Ehrhardt (2020). Bacterial colonization within the first 6 weeks of life and pulmonary outcome in preterm infants < 1000g. Journal of Clinical Medicine 9, 2240, doi: 10.3390/jcm9072240
Vogel, F., N. Vahle, J. Gertheiss, and M. J. Tomasik (2020). Neural network classification of movement patterns in a virtual reality experiment. Proceedings of the 35th International Workshop on Statistical Modelling, Vol. I, 442-445
