Expertises
Mathematics
- Long Range
- Stochastic Volatility
- Simulation Study
- Range Dependence
- Test Statistic
- Change Point Analysis
- Tail Index
- Tail Parameter
Organisaties
Publicaties
Jump to: 2025 | 2024 | 2023 | 2022 | 2021 | 2020
2025
Optimal 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
A 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 (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.2312.07282Change-point tests for the tail parameter of Long Memory Stochastic Volatility time series (2023)Statistica sinica, 33(3), 2017-2039. Betken, A., Giraudo, D. & Kulik, R.https://doi.org/10.5705/ss.202020.0265
2022
Higher-order approximation for uncertainty quantification in time series analysis (2022)[Working paper › Preprint]. ArXiv.org. Düker, M.-C. & Betken, A.Rank-based change-point analysis for long-range dependent time series (2022)Bernoulli, 28(4), 2209-2233. Betken, A. & Wendler, M.https://doi.org/10.3150/21-BEJ1416
2021
A Bootstrap Test for Independence of Time Series Based on the Distance Covariance (2021)[Working paper › Preprint]. ArXiv.org. Betken, A., Dehling, H. & Kroll, M.https://doi.org/10.48550/arXiv.2112.14091Block Bootstrapping the Empirical Distance Covariance (2021)[Working paper › Working paper]. ArXiv.org. Betken, A., Dehling, H. & Kroll, M.https://arxiv.org/abs/2112.14091Ordinal pattern dependence as a multivariate dependence measure (2021)Journal of multivariate analysis, 186. Article 104798. Betken, A., Dehling, H., Nüßgen, I. & Schnurr, A.https://doi.org/10.1016/j.jmva.2021.104798
2020
Ordinal pattern dependence as a multivariate dependence measure (2020)[Working paper › Working paper]. ArXiv.org. Betken, A., Dehling, H., Münker, I. & Schnurr, A.https://arxiv.org/abs/2012.02445Change-point tests for the tail parameter of Long Memory Stochastic Volatility time series (2020)[Working paper › Preprint]. ArXiv.org. Betken, A., Giraudo, D. & Kulik, R.https://arxiv.org/abs/2006.02667
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Universiteit Twente
Zilverling (gebouwnr. 11), kamer 2047
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Universiteit Twente
Zilverling 2047
Postbus 217
7500 AE Enschede
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