- \n
- Funktionale, kategoriale und hochdimensionale Daten<\/li>\n
- Statistisches und maschinelles Lernen<\/li>\n
- Structural Health Monitoring<\/li>\n<\/ul>\n
\u00a0<\/h3>\n
Ausgew\u00e4hlte Forschungsprojekte:<\/h3>\n\n\n
![\"Ordinale](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2021\/11\/ordData.png\")
<\/figure>\nDFG-Sachbeihilfe: Statistische Methoden und Modelle f\u00fcr Abh\u00e4ngige Kategoriale, insbesondere Ordinale Daten<\/em><\/p>\n\n\n\n
Zur Analyse und Modellierung hochdimensionaler voneinander abh\u00e4ngiger Variablen existieren unterschiedlichste statistische Methoden, wie etwa grafische Modelle oder Hauptkomponentenanalyse. Diese erfordern f\u00fcr gew\u00f6hnlich jedoch stetige bzw.<\/abbr> metrisch-skalierte Daten. Entsprechende Methoden f\u00fcr kategoriale, insbesondere ordinale Daten stehen dagegen weit weniger zur Verf\u00fcgung, obwohl man diese Art von Daten h\u00e4ufig und in verschiedensten Anwendungen findet. Das Ziel des Projektes ist es daher, diese L\u00fccke in der statistischen Methodik zu schlie\u00dfen, indem wir geeignete Methoden entwickeln, wie z.B.<\/abbr> regularisierte grafische Modelle und Hauptkomponentenanalyse f\u00fcr ordinale Variablen.<\/p>\n\n\n
\n![\"\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/03\/dfg_logo_schriftzug_blau_foerderung_4c-2-1024x290.jpg\")
<\/figure>\n<\/div><\/div><\/div>\n\n\n\nBearbeitung: Prof.<\/abbr> Dr.<\/abbr> Jan Gertheiss; Aisouda Hoshiyar, M.Sc.<\/abbr>; Ejike Richard Ugba, M.Sc.<\/abbr> <\/p>\n\n\n\n
<\/p>\n\n\n\n
F\u00f6rderzeitraum: 2019 \u2013 2022<\/p>\n\n\n\n
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<\/div>\n\n\n\n![\"Br\u00fccke\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/07\/TBEisern.jpg\")
<\/figure>\nTeilprojekt Data Analytics<\/em> im dtec.bw Verbundprojekt SHM \u2013 Digitalisierung und \u00dcberwachung von Infrastrukturbauwerken<\/em> <\/p>\n\n\n\n
Ziel des Gesamtprojekts Structural Health Monitoring<\/em> (SHM) ist die zuverl\u00e4ssigkeitsbasierte Zustandsbewertung bestehender und eventuell gesch\u00e4digter Infrastrukturbauwerke mittels unterschiedler Monitoringsysteme in einem integrierten digitalen System (Details<\/a>).<\/p>\n\n\n\n
Im Teilprojekt Data Analytics untersuchen wir insbesondere zeitlich-r\u00e4umliche Abh\u00e4ngigkeitsstrukturen innerhalb bzw.<\/abbr> zwischen Sensorstr\u00f6men und entwickeln\/adaptieren Methoden des maschinellen Lernens zur Feature Extraction und Schadenserkennung.<\/p>\n\n\n
\n![\"\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/03\/dtec.bw_EU-Foederhinweis_RGB_vertikal-1024x241.jpg\")
<\/figure>\n<\/div><\/div><\/div>\n\n\n\nBearbeitung: 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
F\u00f6rderzeitraum: 2021 \u2013 2026<\/p>\n\n\n\n
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![\"Korrelation\"](\"https:\/\/www.hsu-hh.de\/statdat\/wp-content\/uploads\/sites\/794\/2023\/06\/CondCorr-1.png\")
<\/figure>\nDas Projekt HPC f\u00fcr semiparametrische statistische Modellierung auf massiven Datens\u00e4tzen<\/em> ist eine wichtige Erg\u00e4nzung und Erweiterung f\u00fcr das dtec.bw-Projekt SHM \u2013 Digitalisierung und \u00dcberwachung von Infrastrukturbauwerken<\/a>. Angesichts der enormen Gr\u00f6\u00dfe der Datens\u00e4tze (mehrere Jahre hochaufgel\u00f6ster Sensordaten) freuen wir uns \u00fcber die Zusammenarbeit mit dem hpc.bw-Team auf dem HSUper Cluster<\/a> <\/p>\n\n\n\n
Das Hauptziel des Projektes ist die effiziente Implementierung von Sch\u00e4tzungen semiparametrischer und nichtparametrische Modelle zur \u00dcberwachung und Erkennung von strukturellen \u00c4nderungen.<\/p>\n\n\n\n
Diese Zusammenarbeit verbessert die Effizienz und Skalierbarkeit der datenanalytischen Modellierungsprozesse und tr\u00e4gt somit zum breiteren Bereich der Infrastruktur\u00fcberwachung bei.<\/p>\n\n\n
\n<\/figure>\n<\/div><\/div><\/div>\n\n\n\nBearbeitung: Dr.<\/abbr> Philipp Wittenberg; Lizzie Neumann, M.Sc.<\/abbr><\/p>\n\n\n\n
F\u00f6rderzeitraum: 2023 \u2013 2024 <\/p>\n\n\n\n
<\/div>\n\n\n\nAktuelle Ver\u00f6ffentlichungen:<\/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. Edited by Eirik Bjorheim Abrahamsen, Terje Aven, Frederic Bouder, Roger Flage, Marja Yl\u00f6nen. Research Publishing, Singapore<\/em>, 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<\/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>Hoshiyar<\/strong>,\u00a0A<\/strong>., Gertheiss, L.H. and Gertheiss, J. (2024). Regularization and Model Selection for Ordinal-on-Ordinal Regression with Applications to Food Products‘ Testing and Survey Data. Preprint.
<\/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. und 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>,\u00a0A.\u00a0<\/strong>and Gertheiss, J. (2023). Fusion, Smoothing and Model Selection for Item-on-Item Regression. In: Proceedings of the 37th International Workshop on Statistical Modelling, Dortmund, Germany, 133\u2013138.
<\/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