Marie-Christine Düker

Research Interests: high-dimensional statistics, time series analysis, functional data analysis, concentration phenomena, and extreme value theory, applications in economics, psychology, chemistry and ecology.

About

I'm an assistant professor for Mathematical Statistics and Data Science at Friedrich-Alexander University. Prior to that I was a postdoctoral associate in the Department of Statistics and Data Science at Cornell University, working with David Matteson.

I received my PhD in Mathematics at the Faculty of Mathematics at Ruhr-University Bochum working in Herold Dehlings group and spent my PhD part-time in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill under the supervision of Vladas Pipiras.

My current research interests include high-dimensional statistics, time series analysis, discrete data modelling, machine learning, nonlinear dynamics, dimension reduction, change-point analysis, functional data analysis, (multivariate) long-range dependence, nonstationary data and extreme value theory, applications in econometrics, neuroscience, chemistry and ecology.

CV

A CV can be found here.

Publications

Preprints

Düker, M., Waterbury, A.: Kernel estimation for nonlinear dynamics, 2025. [arXiv]

Jauch, M., Düker, M., Hoff, P.: Prior distributions for structured semi-orthogonal matrices, 2025. [arXiv]

Düker, M., Zoubouloglou, P.: Breuer-Major Theorems for Hilbert Space-Valued Random Variables, 2024. [arXiv].

Kim, Y, Düker, M., Fisher, Z. F., Pipiras, V.: Latent Gaussian dynamic factor modeling and forecasting for high-dimensional count time series, 2024. [arXiv].

Düker, M., Pipiras, V.: Testing for common structures in high-dimensional factor models, 2024. [arXiv] [Supplement].

Betken, A., Düker, M.: Higher order approximation for constructing confidence intervals in time series, 2023. [arXiv].

Düker, M.: Sample autocovariance operators of long-range dependent Hilbert space-valued linear processes, 2022. [Link]

Peer-Reviewed Papers

Düker, M., Matteson, D., Tsay, R., Wilms, I.: Vector AutoRegressive Moving Average Models: A Review, To appear in WIREs Computational Statistics, 2025+. [arXiv].

Düker, M., Lund, R., Pipiras, V.: High-dimensional latent Gaussian count time series: Concentration results for autocovariances and applications, 2024. Electronic Journal of Statistics [Link] [arXiv].

Xu, Y., Düker, M., Matteson, D.: Testing Simultaneous Diagonalizability. Journal of the American Statistical Association, 2023. [Link] [arXiv].

Baek, C., Düker, M., Pipiras, V.: Local Whittle estimation of high-dimensional long-run variance and precision matrices. The Annals of Statistics 51(6), 2386-2414, 2023. [Link] [arXiv].

Baek, C., Düker, M., Jeong, S., Lee, T.: Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence. Journal of Statistical Papers, 2023. [Link] [arXiv].

Goolsby, C., Losey, J., Xu, Y., Düker, M., Sherman Getmansky, M., Matteson, D. S., Moradi, M.: Addressing the Embeddability Problem in Transition Rate Estimation. The Journal of Physical Chemistry A, 127(27), 5745-5759, 2023. [Link] [bioRxiv].

Davidow, M., Cory, M., Che-Castaldo, J., Schafer, T., Düker, M., Corcoran, D., Matteson, D. S.: Clustering Future Scenarios Based on Predicted Range Maps. Methods in Ecology and Evolution, 2023. [Link] [arXiv].

Schafer, T., McGranaghan, R., Getmansky Sherman, M., Feng, M., Owolabi, O., Ryan, S., Düker, M., Jauch, M., Matteson, D. S.: Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research. KDD Proceedings, 2021.

Düker, M., Pipiras, V., Sundararajan, R.: Cotrending: testing for common deterministic trends in varying means model. Journal of Multivariate Analysis 187, 104825, 2021. [doi] [Link] [Supplement].

Düker, M.: Limit theorems for multivariate long-range dependent processes. Stochastic Processes and their Applications 130, 5394–5425, 2020. [doi] [arXiv].

Düker, M., Pipiras, V.: Asymptotic results for multivariate local Whittle estimation with applications. 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019. [doi] [Supplement].

Düker, M.: Limit theorems for Hilbert space-valued linear processes under long-range dependence. Stochastic Processes and their Applications, 128(5):1439–1465, 2018. [doi].

Dissertation

Düker, M.: High-dimensional time series under long-range dependence and nonstationarity. Dissertation, 2020.

Teaching

• Winter term 2024/25, High-dimensional time series
• Winter term 2024/25, Seminar in Statistical foundations of Data Science
• Summer 2024, Introduction to Mathematical data analysis
• Summer 2024, Seminar in Statistical foundations of Data Science
• Winter term 2023/24, Special topics class in highdimensional Statistics
• Winter term 2023/24, Seminar in Statistical foundations of Data Science
• Summer term 2023, Special topics class in highdimensional Statistics

Presentations

• 18th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, 14--16th December 2024 (invited).
• Seminar, Department of Mathematics, Otto-von-Guericke University Magdeburg, November 2024 (invited).
• 7th International Conference on Econometrics and Statistics (EcoSta 2024), Beijing, 17–19th July 2024 (invited).
• 16th International Conference of the ERCIM WG on Computational and Methodological Statistics, Berlin, 16–18th December 2023 (invited).
• Seminar, Department of Mathematics, University of Athens, December 2023 (invited).
• Seminar, Department of Mathematics, EPFL, November 2023 (invited).
• NBER-NSF Time Series Conference, Montreal, September 2023.
• Joint Statistical Meetings (JSM), Toronto, August 2023 (invited).
• 6th International Conference on Econometrics and Statistics (EcoSta 2023), Waseda University Tokyo, virtual, August 2023 (invited).
• 13th Extreme Value Analysis Conference, Bocconi University Milan, June 2023 (invited).
• Conference "Women in Data Science", Friedrich-Alexander University Erlangen, virtual, April 2023 (invited).
• Seminar, Department of Statistics and Mathematics, University of Massachusetts at Amherst, March 2023 (invited).
• Conference “Adaptive and high-dimensional spatio-temporal methods for forecasting", CIRM Luminy, September 2022 (invited).
• Conference, UP-STAT 2022 Hybrid Conference, Buffalo, May 2022 (invited).
• Seminar, Department of Statistics and Data Science, Cornell University, March 2022.
• Seminar, Econometrics and Statistics, University of Chicago, Booth School of Business, virtual, March 2022 (invited).
• Seminar, Department of Statistics, University of Wisconsin Madison, virtual, February 2022 (invited).
• Seminar, Department of Statistics, University of Michigan, January 2022 (invited).
• Conference, 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, virtual, 18–20th December 2021 (invited).
• Stochastics Seminar, Department Mathematics, University of Utah, October 2021.
• Conference, 10th World Congress, virtual, July 19th–23rd.
• Graduate Seminar, Department of Statistics and Operations Research, UNC Chapel Hill, September 2019.
• Conference, 40th Conference on Stochastic Processes and their Applications, Gothenburg, 11–15th of June 2018.
• 13th German Probability and Statistics Days, Freiburg, 27th February - 02nd March 2018.
• Conference, 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, 16-18th December 2017.
• Graduate Seminar, Department of Statistics and Operations Research, TU Dortmund, November 2017.
• Conference, 31st European Meeting of Statisticians, Helsinki, 24-28th July 2017.

Contact

Friedrich-Alexander University
Department of Statistics and Data Science
Tech campus
Cauerstr. 11
Erlangen, Germany

E-Mail: marie.dueker@fau.de