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Business and Social Statistics
Department of Statistics
Studenten im Sommer auf dem Campus Nord, rote Zahnräder im Hintergrund © Roland Baege​/​TU Dortmund
Postdoc

Dr. Mirko Alexander Jakubzik

Contact

TU Dortmund University
Department of Statistics
Chair of Business and Social Statistics
CDI Building, Room 3
44221 Dortmund
Germany

E-mail: mirko.jakubziktu-dortmundde

Portrait von Mirko Alexander Jakubzik © Mirko Alexander Jakubzik​/​privat
Portrait von Mirko Alexander Jakubzik
  • since February 2024: Postdoc and research associate
  • 2017-2024: Doctorate in Statistics, TU Dortmund University, Chair of Statistics with Applications in Engineering Sciences
  • 2014-2017: M.Sc. Mathematics (minor in Statistics), TU Dortmund University
  • 2011-2014: B.Sc. Mathematics (minor in Statistics), TU Dortmund University
  • Robust statistics (esp. data and regression depth, K-sign depth, model fitting and contamination)
  • Intensity-based modeling and inference (esp. load sharing & damage accumulation, minimum distance estimation, random time transformation)
  • Non-parametric and semi-parametric methods
  • Survival analysis
  • Experimental design
  • Leckey, K., Jakubzik, M., und Müller, C.H. (2023). On the consistency of K-sign depth tests. Econometrics and Statistics. doi.
  • Abbas, S., Fried, R., Heinrich, J., Horn, M., Jakubzik, M., Kohlenbach, J., Maurer, R., Michels, A. und Müller, C.H. (2019). Detection of anomalous sequences in crack data of a bridge monitoring. In: Applications in Statistical Computing - From Music Data Analysis to Industrial Quality Improvement. Eds. K. Ickstadt, H. Trautmann, G. Szepannek, N. Bauer, K. Lübke, M. Vichi, Springer, 251-269.

Dissertation:

  • Jakubzik, M. (2023). Statistical inference for intensity-based load sharing models with damage accumulation. doi.
  • Sign Depth for Intensity-Based Point Process Models. Bernoulli-IMS Worldcongress 2024. Bochum, Germany (08/2024).
  • Application of Sign Depth to Point Process Models. ICORS 2024. Fairfax, VA, USA (07/2024).
  • Application of Sign Depth to Point Process Models. ENBIS Spring Meeting 2024. Dortmund, Germany (05/2024).
  • Consistency of the 3-Sign Depth Test for Intensity-Based Point Process Models. Statistische Woche 2023. Dortmund, Germany (09/23).
  • Vorzeichen-Tiefen: Anpassungsmaße in der Regressionsanalyse. SFB 823-Workshop "Strukturbruch". Bochum, Germany (10/19). Gemeinsamer Vortrag mit Dennis Malcherczyk.
  • Semi-Parametric and Robust Estimation in Intensity-Based Models. Statistische Woche 2019. Trier, Germany (09/19).
  • Statistical Inference for Intensity-Based Load Sharing Models with Damage Accumulation. 15. Doktorandentreff Stochastik. Darmstadt, Germany (07/19).
  • A Minimum Distance Estimator for Intensity-based Load Sharing Models with Damage Accumulation. 11th International Conference on Mathematical Methods in
    Reliability.     Hongkong (06/19).
  • Applications of a Minimum Distance Estimator for Self-Exciting Counting Processes. DAGStat Conference 2019. Munich, Germany (03/19).
  • Applications of a minimum distance estimator for specific self-exciting point processes. Statistische Woche 2018. Linz, Austria (09/18).
  • Applications of a minimum distance estimator for specific self-exciting point processes. 14. Doktorandentreff Stochastik. Essen, Germany (08/18).
  • Applications of a minimum distance estimator for specific self-exciting point processes. German Probability and Statistics Days 2018. Freiburg, Germany (03/18).
  • Asymptotic properties of a minimum distance estimator for self-exciting point processes. Nachwuchsworkshop der DStatG, Statistische Woche 2017. Rostock, Germany (09/17).
  • Schätzen und Testen (WiSe 22/23 & WiSe 24/25)
  • Statistical Methods for Counting Processes (SoSe 24)
  • Wahrscheinlichkeitstheorie (WiSe 18/19 & WiSe 19/20 & WiSe 23/24)
  • Nichtparametrik und robuste Statistik (SuSe 23)
  • Fortgeschrittene Versuchsplanung (SuSe 17 & SoSe 19 & SuSe 21 & SuSe 23)
  • Fallstudien 2 (SuSe 17 & WiSe 19/20 & WiSe 22/23)
  • Elementare Wahrscheinlichkeitsrechnung (SuSe 20 & SuSe 22)
  • Grundlagen der Versuchsplanung (SuSe 20 & SuSe 22)
  • Vektor- und Matrizenrechnung (WiSe 21/22)
  • Statistik der Materialermüdung & Zuverlässigkeit (WiSe 17/18 & WiSe 19/20 & WiSe 21/22)
  • Optimalität bei Schätzern und Tests (SuSe 21)
  • Schätzen und Testen 1 (WiSe 20/21)
  • Fallstudien 1 (SuSe 19)
  • Statistik 2 (SuSe 18)
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