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- Functional, categorical, and high-dimensional data<\/li>\n
- Statistical and machine learning<\/li>\n
- Structural Health Monitoring<\/li>\n<\/ul>\n
\u00a0<\/h3>\n
Selected Research Projects:<\/h3>\n\n\n
![\"Ordinal](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2021\/11\/ordData-1.png\")
<\/figure>\nDFG Research Grant: Statistical Methods and Models for Interdependent Categorical, particularly Ordinal Data<\/em><\/p>\n\n\n\n
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<\/p>\n\n\n
\n![\"\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/03\/dfg_logo_schriftzug_blau_foerderung_4c-1024x290.jpg\")
<\/figure>\n<\/div><\/div><\/div>\n\n\n\nResearch Group: Prof.<\/abbr> Dr.<\/abbr> Jan Gertheiss; Aisouda Hoshiyar, M.Sc.<\/abbr>; Ejike Richard Ugba, M.Sc.<\/abbr> <\/p>\n\n\n\n
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Funding Period: 2019 \u2013 2022<\/p>\n\n\n\n
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![\"bridge\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/07\/TBEisern.jpg\")
<\/figure>\nSubproject Data Analytics<\/em>, Joint dtec.bw Research Project SHM \u2013 Digitization and Monitoring of Bridge Infrastructure<\/em> <\/p>\n\n\n\n
Within the joint research project Structural Health Monitoring<\/em> (SHM) we aim at assessing existing and potentially damaged highway bridges by means of different monitoring systems in an integrated, digital framework (details<\/a>).<\/p>\n\n\n\n
In our subproject Data Analytics<\/em> we investigate spatio-temporal associations within and between sensor streams and develop\/adapt machine learning methods for feature extraction and damage detection.<\/p>\n\n\n
\n![\"\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/03\/dtec.bw_EU-Foederhinweis_RGB_vertikal-1-1024x241.jpg\")
<\/figure>\n<\/div><\/div><\/div>\n\n\n\nResearch Group: Prof.<\/abbr> Dr.<\/abbr> Jan Gertheiss, Lizzie Neumann, M.Sc.<\/abbr>; Frederike Vogel, M.Sc.<\/abbr>; Dr.<\/abbr> Philipp Wittenberg <\/p>\n\n\n\n
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Funding Period: 2021 \u2013 2026<\/p>\n\n\n\n
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<\/div>\n\n\n\n![\"correlation\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/06\/CondCorr-1.png\")
<\/figure>\nThe project HPC for semi-parametric statistical modeling on massive datasets<\/em> is an important addition and extension for the dtec.bw project SHM – Digitization and Monitoring of Bridge Infrastructure<\/a>. 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<\/a>. <\/p>\n\n\n\n
The project’s main goal is to efficiently implement estimation of semi-parametric and non-parametric models for structural change monitoring and detection.<\/p>\n\n\n\n
This collaboration improves the efficiency and scalability of data analytic modeling processes, contributing to the broader field of infrastructure monitoring.<\/p>\n\n\n
\n<\/figure>\n<\/div><\/div><\/div>\n\n\n\nResearch Group: Dr.<\/abbr> Philipp Wittenberg, Lizzie Neumann, M.Sc.<\/abbr><\/p>\n\n\n\n
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Funding Period: 2023 \u2013 2024 <\/p>\n\n\n\n
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<\/div>\n\n\n\nRecent publications:<\/h3>\n\n\n\n
![\"Publikation](\"\/wp-content\/themes\/hsu\/img\/dummy\/downloads_dummy.png\")
<\/div>Hoshiyar, A.<\/strong>, L.H. Gertheiss, and J. Gertheiss<\/strong> (2026). Regularization and model selection for ordinal-on-ordinal regression with applications to food products\u2019 testing and survey data. Statistical Modelling<\/em>. doi: 10.1177\/1471082X251391582<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong> and A. Groll (2025). Penalisierte Regression. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik<\/em>. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007\/978-3-662-63496-7_12-1<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong>, L. Neumann<\/strong>, and P. Wittenberg<\/strong> (2025). Bridge Health Monitoring Under Varying Environmental Conditions Using Conditional Principal Component Analysis. In: Cunha, \u00c1., Caetano, E. (eds) Experimental Vibration Analysis for Civil Engineering Structures<\/em>. EVACES 2025. Lecture Notes in Civil Engineering, vol 675. Springer, Cham. doi: 10.1007\/978-3-031-96106-9_43<\/a><\/div><\/div>\n\n\n\n<\/div>Neumann, L.<\/strong>, P. Wittenberg<\/strong>, A. Mendler, and J. Gertheiss<\/strong> (2025). Confounder-adjusted covariances of system outputs and applications to structural health monitoring. Mechanical Systems and Signal Processing<\/em> 224, 111083, doi: 10.1016\/j.ymssp.2024.111983<\/a><\/div><\/div>\n\n\n\n<\/div>Neumann, L.<\/strong>, P. Wittenberg<\/strong>, and J. Gertheiss<\/strong> (2025). Confidence Intervals for Conditional Covariances of Natural Frequencies. In: Proceedings of the IOMAC 2025 (to appear<\/a>)<\/div><\/div>\n\n\n\n<\/div>Neumann, L.<\/strong> (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. <\/em>Edited by Eirik Bjorheim Abrahamsen, Terje Aven, Frederic Bouder, Roger Flage, Marja Yl\u00f6nen. Research Publishing, Singapore, 2985-2992, doi:10.3850\/978-981-94-3281-3-procd<\/a><\/div><\/div>\n\n\n\n<\/div>Siebenmorgen, C., M.S. Gr\u00f8nbeck, A. Schubert<\/strong>, J. Gertheiss,<\/strong> and J. M\u00f6rlein (2025). Updating descriptive sensory evaluation of chicken: proposing new protocols and statistical analysis. Poultry Science<\/em>, 104(11), 105807, doi: 10.1016\/j.psj.2025.105807<\/a><\/div><\/div>\n\n\n\n<\/div>Tu, D., J. Wrobel, T.D. Satterthwaite, J. Goldsmith, R.C. Gur, R.E. Gur, J. Gertheiss,<\/strong> D.S. Bassett, and R.T. Shinohara (2025). Regression and alignment for functional data and network topology. Biostatistics<\/em>, 26(1), kxae026, doi: 10.1093\/biostatistics\/kxae026<\/a><\/div><\/div>\n\n\n\n<\/div>Wittenberg P.<\/strong>, A. Mendler, S. Knoth, and J. Gertheiss<\/strong> (2025). Multivariate Long-Term Profile Monitoring with\u00a0Application to\u00a0the\u00a0KW51 Railway Bridge. In: Cunha, \u00c1., Caetano, E. (eds) Experimental Vibration Analysis for Civil Engineering Structures<\/em>. EVACES 2025. Lecture Notes in Civil Engineering, vol 676. Springer, Cham. doi: 10.1007\/978-3-031-96114-4_48<\/a><\/div><\/div>\n\n\n\n<\/div>Wittenberg P.<\/strong>, L. Neumann L.<\/strong>, A. Mendler, and J. Gertheiss<\/strong> (2025). Covariate-adjusted functional data analysis for structural health monitoring. Data-Centric Engineering<\/em>, 6:e27, doi: 10.1017\/dce.2025.18<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong>, D. R\u00fcgamer, and S. Greven (2024). Methoden f\u00fcr die Analyse funktionaler Daten. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik<\/em>. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007\/978-3-662-63496-7_5-1<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong>, D. R\u00fcgamer, B.X.W. Liew, and S. Greven (2024). Functional Data Analysis: An Introduction and Recent Developments. Biometrical Journal<\/em>, 66: e202300363, doi: 10.1002\/bimj.202300363<\/a><\/div><\/div>\n\n\n\n<\/div>Vogel, F.<\/strong>. (2024). Examining Quantiles in Structural Health Monitoring. In: Proceedings of the 10th European Workshop on Structural Health Monitoring (EWSHM 2024)<\/em>, e-Journal of Nondestructive Testing, doi: 10.58286\/29664<\/a><\/div><\/div>\n\n\n\n<\/div>Windmann, A., Wittenberg, P.<\/strong>, 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)<\/em>, Beijing, China, 2024, pp. 1-8 doi: 10.1109\/INDIN58382.2024.10774364<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong> 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)<\/em>, 532 – 540, doi: 10.1137\/1.9781611977653.ch60<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong> and G. Tutz (2023). Generalisierte lineare und gemischte Modelle. In: Gertheiss, J., Schmid, M., Spindler, M. (eds) Moderne Verfahren der Angewandten Statistik<\/em>. Springer Spektrum, Berlin, Heidelberg. doi: 10.1007\/978-3-662-63496-7_1-1<\/a><\/div><\/div>\n\n\n\n<\/div>Gertheiss, J.<\/strong> 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<\/em>. Statistics for Social and Behavioral Sciences. Springer, Cham, 199-232, doi: 10.1007\/978-3-031-31186-4_7<\/a><\/div><\/div>\n\n\n\n<\/div>Hesselmann, C., D. Reinhardt, J. Gertheiss<\/strong>, and J.P. M\u00fcller (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<\/abbr> Systems<\/em>, NordSec 2022, Lecture Notes in Computer Sciences. Springer, Cham, 20-39, doi: 10.1007\/978-3-031-22295-5_2<\/a><\/div><\/div>\n\n\n\n<\/div>Hoshiyar<\/strong>, A.<\/strong>, H.A.L. Kiers, and J. Gertheiss<\/strong> (2023).\u00a0Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets. British Journal of Mathematical and Statistical Psychology<\/em> 76(2), 353-371, doi: 10.1111\/bmsp.12297<\/a><\/div><\/div>\n\n\n\n<\/div>M.C. Morais, P. Wittenberg<\/strong> and S. Knoth (2023).\u00a0An ARL-unbiased modified chart for monitoring autoregressive counts with geometric marginal distributions. Sequential Analysis<\/em> 42(3), 323-347, doi: 10.1080\/07474946.2023.2221996<\/a><\/div><\/div>\n\n\n\n<\/div>M.C. Morais, P. Wittenberg<\/strong> and C.J. Cruz (2023).\u00a0An ARL-Unbiased Modified np-Chart for Autoregressive Binomial Count. Stochastics and Quality Control<\/em> 38(1), 11-24, doi: 10.1515\/eqc-2022-0052<\/a><\/div><\/div>\n\n\n\n<\/div>Neumann, L.<\/strong> (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.<\/div><\/div>\n\n\n\n<\/div>Selk, L.<\/strong> and J. Gertheiss<\/strong> (2023). Nonparametric regression and classification with functional,\u00a0categorical, and mixed covariates. Advances in Data Analysis and Classification<\/em> 17(2), 519-543, doi: 10.1007\/s11634-022-00513-7<\/a><\/div><\/div>\n\n\n\n