Zilverling

dr. A. Betken PhD (Annika)

Assistant Professor
EEMCS-AM-MOR
Zilverling 2047
a.betken@utwente.nl
Business card

My personal webpage: https://annikabetken.github.io

Expertise

  • Mathematics

    • Time Series Analysis
    • Time Series
    • Simulation Study
    • Test Statistic
    • Asymptotic Distribution
    • Empirical Process
    • Test Point
    • Change Point Analysis

Organisations

Publications

Jump to: 2026 | 2025 | 2024 | 2023

2026

A two-sample test based on averaged Wilcoxon rank sums over interpoint distances (2026)Journal of multivariate analysis, 215. Article 105659. Betken, A., Marjanovic, A. & Proksch, K.https://doi.org/10.1016/j.jmva.2026.105659Ordinal patterns based time series analysis and applications in privacy (2026)[Thesis › PhD Thesis - Research UT, graduation UT]. Micali, G.https://doi.org/10.3990/1.9789036572583Central limit theorems for the outputs of fully convolutional neural networks with time series input (2026)[Working paper › Preprint]. ArXiv.org. Betken, A., Micali, G. & Schmidt-Hieber, J.Symmetry Testing in Time Series using Ordinal Patterns: A U-Statistic Approach (2026)[Working paper › Preprint]. ArXiv.org. Betken, A., Micali, G. & Ruiz-Marín, M.

2025

Ordinal pattern-based change point detection (2025)Test, 34(4), 927–980. Betken, A., Micali, G. & Schmidt-Hieber, J.https://doi.org/10.1007/s11749-025-00983-9Extending Characterizations of Multivariate Laws via Distance Distributions (2025)[Working paper › Preprint]. ArXiv.org. Betken, A., Marjanovic, A. & Proksch, K.https://doi.org/10.48550/arXiv.2511.04870Statistical machine learning beyond standard supervised learning (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Wen, H.https://doi.org/10.3990/1.9789036568203Higher-order approximation for uncertainty quantification in time-series analysis (2025)Econometric Theory, 1-50 (E-pub ahead of print/First online). Betken, A. & Düker, M. C.https://doi.org/10.1017/S0266466625100054On the Robustness of Kernel Ridge Regression Using the Cauchy Loss Function (2025)[Working paper › Preprint]. ArXiv.org. Wen, H., Betken, A. & Koolen, W.https://doi.org/10.48550/arXiv.2503.20120Ordinal Patterns Based Change Points Detection (2025)[Working paper › Preprint]. ArXiv.org. Betken, A., Micali, G. & Schmidt-Hieber, J.https://doi.org/10.48550/arXiv.2502.03099Median of Forests for Robust Density Estimation (2025)[Working paper › Preprint]. ArXiv.org. Wen, H., Betken, A. & Huang, T.https://doi.org/10.48550/arXiv.2501.15157Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios (2025)[Contribution to conference › Paper] 13th International Conference on Learning Representations, ICLR 2025. Betken, A.

2024

Differentially Private Algorithms for Linear Queries via Stochastic Convex Optimization (2024)[Working paper › Preprint]. ArXiv.org. Micali, G., Lezane, C. & Betken, A.https://doi.org/10.48550/arXiv.2411.00921A two-sample test based on averaged Wilcoxon rank sums over interpoint distances (2024)[Working paper › Preprint]. ArXiv.org. Betken, A., Marjanovic, A. & Proksch, K.https://doi.org/10.48550/arXiv.2408.10570Depth Patterns and their Applications in Animal Tracking (2024)[Working paper › Preprint]. ArXiv.org. Betken, A. & Schnurr, A.https://doi.org/10.48550/arXiv.2401.13532Class Probability Matching with Calibrated Networks for Label Shift Adaptation (2024)In 12th International Conference on Learning Representations. Betken, A., Wen, H. & Hang, H.https://openreview.net/forum?id=mliQ2huFrZClass Probability Matching with Calibrated Networks for Label Shift Adaption (2024)[Contribution to conference › Poster] 12th International Conference on Learning Representations, ICLR 2024. Betken, A.Higher-order approximation for constructing confidence intervals in time series (2024)[Working paper › Preprint]. ArXiv.org. Betken, A. & Düker, M.-C.https://doi.org/10.48550/arXiv.2211.01108Test for independence of long-range dependent time series using distance covariance (2024)[Working paper › Preprint]. ArXiv.org. Betken, A. & Dehling, H.https://doi.org/10.48550/arXiv.2107.03041

2023

Class Probability Matching Using Kernel Methods for Label Shift Adaptation (2023)[Working paper › Preprint]. ArXiv.org. Wen, H., Betken, A. & Hang, H.https://doi.org/10.48550/arXiv.2312.07282

Other contributions

Betken, A., Dehling, H., Münker, I., Schnurr, A. (2020): Ordinal pattern dependence as a multivariate dependence measure. Preprint, arXiv:2012.02445

Betken, A., Giraudo, D., Kulik, R. (2020): Change-point tests for the tail parameter of Long Memory Stochastic Volatility time series. Preprint, arXiv:2006.02667

Betken, A., Wendler, M. (2020): Rank-based change-point analysis for long-range dependent time series. Preprint, arXiv:2004.06574

Betken, A., Buchsteiner, J., Dehling, H., Münker, I., Schnurr, A., Woerner, J. H. C. (2019): Ordinal Patterns in Long-Range Dependent Time Series. Scandinavian Journal of Statistics, doi: 10.1111/sjos.12478

Betken, A. (2017): Change point estimation based on Wilcoxon tests in the presence of long-range dependence. Electronic Journal of Statistics, 11(2), 3633-3672, doi: 10.1214/17-EJS1323

Betken, A., Kulik, R. (2019): Testing for change in stochastic volatility with long range dependence. Journal of Time Series Analysis, 40(5), 707-738, doi: 10.1111/jtsa.12449

Betken, A., Wendler, M. (2018): Subsampling for General Statistics under Long Range Dependence. Statistica Sinica, 28(3), 1199-1224, doi: 10.5705/ss.202015.0435

Betken, A. (2016): Testing for change-points in long-range dependent time series by means of a self-normalized Wilcoxon test. Journal of Time Series Analysis, 37(6), 785-809, doi: 10.1111/jtsa.12187

Research profiles

Address

University of Twente

Zilverling (building no. 11), room 2047
Hallenweg 19
7522 NH Enschede
Netherlands

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