Prof. Dr. Anja Schmiedt

• Applied Mathematics
• Statistics
• Actuarial Science

https://elearning.oth-regensburg.de/course/index.php?categoryid=2632

• Statistik, B.Sc. Wirtschaftsinformatik, WS 2025/26
• Statistical Learning in Actuarial Science (in English), M.Sc. Mathematik, M.Sc. Mathematics for Business and Industry, WS 2025/26
• Project Module, M.Sc. Mathematik, M.Sc. Mathematics for Business and Industry, WS 2025/26  
• Risk Theory (in English), M.Sc. Mathematik, M.Sc. Mathematics for Business and Industry, SS 2025
• Versicherungsmathematik 1, B.Sc. Mathematik, SS 2025
• Statistik, B.Sc. Wirtschaftsinformatik, SS 2025
• Statistical Learning in Actuarial Science (in English), M.Sc. Mathematik, M.Sc. Mathematics for Business and Industry, WS 2024/25 
• Versicherungsmathematik 2, B.Sc. Mathematik, WS 2024/25

(English translation)

• Program: Second round of the program “Teaching Lab to the Power of 3 – Team-based Development of Teaching in Higher Education” organized by the Research and Innovation Lab for Digital Teaching, funded by the Bavarian State Ministry of Science and the Arts and the Bavarian Industry Association as part of the “NewNormal” program (individual application)
• Project period: March 2024 to October 2024 (completed)

(English translation)

• Program: Program for the promotion of teaching projects at Rosenheim Technical University of Applied Sciences, funded by the Foundation Innovation in Higher Education (individual application)
• Project period: March 2023 to September 2023 (completed)

• Mathematical statistics
• Extreme value and record statistics
• Statistical and machine learning
• Statistical models of ordered data
• Applied statistics

• Head of the Laboratory for Statistics and Actuarial Science

• Member of the Doctoral Research Center for Applied Computer Science and Mathematics (PZAI)
• Member of the Regensburg Center for Artificial Intelligence (RCAI)

• Funding body: Bavarian State Ministry of Science and the Arts (7^th funding round of the Program for the Promotion of Applied Research and Development at Universities of Applied Sciences and Technical Universities) (individual application)
• Project period: January 2024 to December 2026
• Cooperative doctoral program with the Chair of Statistics at RWTH Aachen University

• Balakrishnan, N., & Schmiedt, A.B. (2025+). Improved imputation-type bounds for means of record values for general and symmetric distributions. (With editor.)
• Schmiedt, A.B., Balakrishnan, N., & Cramer, E. (2025+). Generalized chi-squared based goodness-of-fit tests under progressive Type-II censoring for exponential and Weibull distributions. (Under review.)
• Schmiedt, A.B., & Cramer, E. (2025+). Adaptive progressive Type-II censoring with random sample size. (Under review.)
• Bedbur, S., Kamps, U., & Schmiedt, A.B. (2026). A flexible model of ordered random variables for non-metallic inclusions in steels and related statistical inference. Applied Mathematical Modelling, 149, 116284. https://doi.org/10.1016/j.apm.2025.116284  
• Schmiedt, A.B., Empacher, C., & Kamps, U. (2025). One- and two-sided prediction intervals for future Pareto record values with applications. Journal of Statistical Theory and Applications, 24, 489-514.http://doi.org/10.1007/s44199-025-00119-w
• Empacher, C., Kamps, U., & Schmiedt, A.B. (2025). Prediction intervals for future Pareto record claims. European Actuarial Journal, 15, 163-197. http://doi.org/10.1007/s13385-024-00397-1
• Schmiedt, A.B., & Weiss, C. (2024). The pair correlation function of multi-dimensional low-discrepancy sequences with small stochastic error terms. Journal of Number Theory, 259, 422-437. https://doi.org/10.1016/j.jnt.2023.12.011
• Schmiedt, A.B., & Cramer, E. (2024). Generalized Ng-Kundu-Chan model of adaptive progressive Type-II censoring and related inference. Naval Research Logistics, 71(3), 389-415. https://doi.org/10.1002/nav.22152
• Schmiedt, A.B. (2016). Domains of attraction of asymptotic distributions of extreme generalized order statistics. Communications in Statistics – Theory and Methods, 45(7), 2089-2104. https://doi.org/10.1080/03610926.2013.870206
• Schmiedt, A.B., Dickert, H.H., Bleck, W., & Kamps, U. (2015). Evaluation of maximum non-metallic inclusion sizes in engineering steels by fitting a generalized extreme value distribution based on vectors of largest observations. Acta Materialia, 95, 1-9. https://doi.org/10.1016/j.actamat.2015.05.013
• Schmiedt, A.B., Dickert, H.H., Bleck, W., & Kamps, U. (2014). Multivariate extreme value analysis and its relevance in a metallographical application. Journal of Applied Statistics, 41(3), 582-595. https://doi.org/10.1080/02664763.2013.845872
• Cramer, E., & Schmiedt, A.B. (2011). Progressively Type-II censored competing risks data from Lomax distributions. Computational Statistics & Data Analysis, 55(3), 1285-1303. https://doi.org/10.1016/j.csda.2010.09.017

• Schmiedt, A.B., Sussmann, M., & Klemcke, J. (2025). Generative Künstliche Intelligenz und Mathematik in der Hochschullehre – Ein Rendezvous zwischen Euphorie und Skepsis. In: B. Zinger, A.M. Wester, & T. Bröker (Hrsg.), Hochschulbildung und Spiel (S. 269-284). transcriptVerlag, Bielefeld. https://www.transcript-verlag.de/978-3-8376-7374-6/
• Neumaier, S., & Schmiedt, A.B. (2025). Über die strukturierte Entwicklung digitaler Lehr- und Lernformate mit EMPAMOS – Erkenntnisse aus einem Lehrprojekt. In: B. Zinger, A.M. Wester, & T. Bröker (Hrsg.), Hochschulbildung und Spiel (S. 121-136). transcriptVerlag, Bielefeld. https://www.transcript-verlag.de/978-3-8376-7374-6/
• Schmiedt, A.B., & Neumaier, S. (2023). Gamification trifft Hybride Lehre: Über ein Lehrprojekt in der mathematischen Statistik. In: H. Dölling, C. Schäfle, S. Kürsten, M. Hunger, J. Hirtt, & P. Riegler (Hrsg.), Tagungsband zum 5. Symposium zur Hochschullehre in den MINT-Fächern (S. 159-166). BayZiel, München. https://doi.org/10.57825/repo_in-4435

• Schmiedt, A.B. (2025). Editorial. In: Deutsche Aktuarvereinigung e.V. (Hrsg.), DAV Journal, 01/25 (S. 333). W. Kohlhammer Druckerei GmbH + Co. KG, Stuttgart. ISSN 0948-7794
• Schmiedt, A.B., & Hüttemann, M. (2024). eXplainable Artificial Intelligence – eine Diskussion und Techniken für Aktuarinnen und Aktuare. In: Deutsche Aktuarvereinigung e.V. (Hrsg.), DAV Journal, 04/24 (S. 273-279). W. Kohlhammer Druckerei GmbH + Co. KG, Stuttgart. ISSN 0948-7794
• Schmiedt, A.B., & Meyerthole, A. (2019). Rechnen mit Feuer in Zeiten von Big Data. Zeitschrift für Versicherungswesen, 10/2019, 311-312.
• Schmiedt, A.B., & Meyerthole, A. (2019). Feuerkumule in der Sachversicherung klug berechnen: Innovativer Algorithmus für die Bestimmungen unter Solvency II. s+s report, 2/2019, 14-15.
• Berg, T., & Schmiedt, A.B. (2018). Die Gretchenfrage der Rückversicherung. Zeitschrift für Versicherungswesen, 21/2018, 629-631.
• Schmiedt, A.B. (2016). Long Term Care: how does the survival behavior of claimants differ. Gen Re Risk Insights, No. 3/2016, 1-5.
• Schmiedt, A.B. (2016). How to handle with care data on disabled lives mortality: a statistical approach. Gen Re Insurance Issues Life/Health, February 2016, 1-28.

• Schmiedt, A.B. (2013). Statistical Modeling of Non-Metallic Inclusions in Steels and Extreme Value Analysis. Dissertation, RWTH Aachen. https://publications.rwth-aachen.de/record/229492
• Schmiedt, A.B. (2010). Optimale Zensierschemata in der progressiven Typ-II Zensierung unter Berücksichtigung von Competing Risks. Diplomarbeit, RWTH Aachen.

• 'Learning from the few: predicting rare events in large-scale actuarial data', 1st ASTIN Bulletin Conference, ETH Zurich, Switzerland, 14.-16.01.2026.
• 'Generalized chi-squared based goodness-of-fit tests under progressive Type-II censoring', McMaster University, Hamilton, Ontario, Kanada, 19.09.2025, eingeladener Vortrag von Prof. N. Balakrishnan (Department of Mathematics and Statistics).
• 'Ordering of random variables via truncation of distributions – application, model, and inference', McMaster University, Hamilton, Ontario, Kanada, 18.09.2025, eingeladener Vortrag von Prof. N. Balakrishnan (Department of Mathematics and Statistics).
• 'Goodness-of-fit tests for exponential and Weibull distributions in progressively Type-II censored data', Statistische Woche 2025, Statistisches Bundesamt, Wiesbaden, 02.-05.09.2025.
• ‘Ordered non-metallic inclusion sizes in steels: model and inference’, 16th International Conference on Order in Statistical Data: Order Statistics and Beyond, RWTH Aachen, 10.-13.06.2025.
• ‘Interval prediction of record values with applications’, 7th Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik, Humboldt-Universität zu Berlin, 24.-28.03.2025.
• 'Prognose von extremen Schadenhöhen mit Rekordstatistiken: Statistische Modellierung, Methoden und Anwendungen‘, Herbsttagung der Deutschen Aktuarvereinigung e.V. und der Deutschen Gesellschaft für Versicherungs- und Finanzmathematik e.V., Mannheim, 18.-19.11.2024.
• ‘Adaptive progressive Type-II censoring in life tests and related inference’, Statistische Woche 2024, Ostbayerische Technische Hochschule Regensburg, 10.-13.09.2024.
• ‘Prediction intervals for future Pareto record values with applications in insurance’, 15th International Conference on Ordered Statistical Data, Universität Coimbra, Portugal, 11.-14.06.2024.
• ‘Modeling and prediction of future record claims’, Statistische Woche 2023, Technische Universität Dortmund, 11.-14.09.2023.
• ‘Generalized Ng-Kundu-Chan model of adaptive progressive Type-II censoring and related inference’, 12th International Conference on Mathematical Methods in Reliability, Universität Murcia, Spanien, 29.05.-02.06.2023, eingeladener Vortrag in der Sektion ‘Statistical inference for ordered and censored lifetime data’ von Prof. Dr. E. Cramer (RWTH Aachen).

• ‚Einsatz von Künstlicher Intelligenz im Finanzdienstleistungsbereich‘, Bundesanstalt für Finanzdienstleistungsaufsicht, Frankfurt, 13.11.2024, eingeladener Vortrag.
• ‚Erklärbare Künstliche Intelligenz: Eine Diskussion für Aktuarinnen und Aktuare‘, Herbsttagung der Deutschen Aktuarvereinigung e.V. und der Deutschen Gesellschaft für Versicherungs- und Finanzmathematik e.V., Hannover, 20.-21.11.2023, gemeinsam mit Dr. S. Hatzesberger (Allianz Private Krankenversicherungs-AG) und Dr. B. Müller (HDI AG).
• ‚Spielend motivieren und gestalten: Mathematikunterricht im Labor für hybride Gruppenarbeiten‘, Snacks aus der Lehre, Technische Hochschule Rosenheim, 24.10.2023.
• ‚Gamification trifft Hybride Lehre‘, 5. Symposium zur Hochschullehre in den MINT-Fächern, Technische Hochschule Nürnberg, 21.-22.09.2023, gemeinsam mit S. Neumaier (TH Rosenheim).   
• ‚Promotion als industrieller Karriereschritt‘, Women Career Lunch, RWTH Aachen, 19.01.2023, eingeladener Vortrag von Prof. Dr. M. Kateri (RWTH Aachen).
• ‚Über die Arbeit von Aktuar:innen in der Versicherungsbranche in Zeiten von Big Data und Artificial Intelligence: Einblicke von Anja Schmiedt (Aktuarin) und ChatGPT (AI)‘, Rotary Club Rosenheim-Innstadt, 16.01.2023, eingeladener Vortrag.  

Explainable Artificial Intelligence: Ein aktueller Überblick für Aktuarinnen und Aktuare (September 2024)

#actuarialdatascience #dav #ads #maschinelleslernen #dgvfm #explainableai #artificialintelligence #erklärbarkeit #wissenschaft

Datenbasierte Sturmmodellierung (September 2023)

#riskmanagement #non-life #climate #analyse #klimarisiken #schadenversicherung #daten #katarstrophenrisiko # wind #sturmschaeden

DENK LAUT – der Podcast: Episode 1 der neuen Reihe FIT4AI(April 2025)

#datascience #ai #artificialintelligence #statistisk #wissenschaft

• Actuarial Education, German Association of Actuaries (DAV), Cologne
• Doctorate in Mathematics, Dr. rer. nat., RWTH Aachen University, overall grade: summa cum laude (awarded the Borchers Plaque for passing the doctoral examination with distinction)
• University Degree in Mathematics, Dipl.-Math., RWTH Aachen University, overall grade:  excellent
• Semester abroad at the Université Savoie Mont Blanc, Chambéry, France
• Vordiplom (Pre-Diploma/ Intermediate Examination) in Mathematics, University of Duisburg-Essen, Duisburg, overall grade: distinction

• Professor of Mathematics (W2), Laboratory for Statistics and Actuarial Science, OTH Regensburg University of Applied Sciences Regensburg (since 09/2024)
• Professor of Mathematics (W2), specializing in stochastics, Rosenheim Technical University of Applied Sciences
• Professor of Statistics, Business Mathematics and Research Methods, University of Applied Management, Ismaning
• Group and Project Manager, Actuarial Consultant, R+V Versicherung AG, Wiesbaden
• Senior Consultant, Actuarial Consultant, Meyerthole Siems Kohlruss Gesellschaft für aktuarielle Beratung mbH, Cologne
• Pricing Actuary, Assistant Account Executive, Actuarial Trainee, General Reinsurance AG, Cologne
• Associate, McKinsey & Company Inc., Düsseldorf
• Research Assistant at the Chair of Statistics, Institute of Statistics, RWTH Aachen University

• Member of the Executive Board
• Member of the “Communication and Contacts” Committee

• Head of the specialist group “Actuarial Data Science / Artificial Intelligence” (co-head)
• Scientific representative on the committee "Actuarial Data Science / Artificial Intelligence" and, in this role, among other things
  • Head of the “Fit4AI” working group (co-head)
  • Head of the “Explainable AI” working group (completed)
  • Member of the “Professional Rules with regard to AI” working group (completed)
• Member of the examination board for the subject “Actuarial Mathematics” in actuarial training

• Reviewer for specialist journals
  • “Applied Stochastic Models in Business and Industry”
  • “Communications in Statistics – Simulation and Computation”
  • “Computational Statistics”
  • “Statistical Papers”
• Memberships
  • Austrian Association of Actuaries (AVÖ)
  • German Association of Actuaries (DAV)
  • German Society for Insurance and Financial Mathematics (DGVFM)
  • DMV Stochastics Specialist Group
  • Doctoral Research Center for Applied Computer Science and Mathematics (PZAI)
  • Regensburg Center for Artificial Intelligence (RCAI)
• Rotary Club Regensburg