| Detecting deviations from second-order stationarity in locally stationary functional time series A Bücher, H Dette, F Heinrichs Annals of the Institute of Statistical Mathematics 72, 1055-1094, 2020 | 21 | 2020 |
| Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators A Bücher, H Dette, F Heinrichs The Annals of Statistics 49 (6), 3583-3617, 2021 | 13 | 2021 |
| A portmanteau-type test for detecting serial correlation in locally stationary functional time series A Bücher, H Dette, F Heinrichs Statistical Inference for Stochastic Processes 26 (2), 255-278, 2023 | 9 | 2023 |
| A distribution free test for changes in the trend function of locally stationary processes F Heinrichs, H Dette Electronic Journal of Statistics 15 (2), 3762-3797, 2021 | 8* | 2021 |
| Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification F Heinrichs, M Heim, C Weber International Conference on Machine Learning, 12866-12881, 2023 | 6 | 2023 |
| Using crisp-dm to grow as data scientist F Heinrichs Towards Data Science. Acesso disponıvel em: https://towardsdatascience. com …, 2020 | 5 | 2020 |
| GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis F Heinrichs arXiv preprint arXiv:2401.15623, 2024 | 3 | 2024 |
| Monitoring Machine Learning Models: Online Detection of Relevant Deviations F Heinrichs arXiv preprint arXiv:2309.15187, 2023 | 3 | 2023 |
| A Portmanteau-type test for detecting serial correlation in locally stationary functional time series A Bücher, H Dette, F Heinrichs arXiv preprint arXiv:2009.07312, 2020 | 1 | 2020 |
| Algorithmen, Zufall, Unsicherheit–und Pizza!: Wie Mathematik uns hilft, alltägliche Entscheidungen zu treffen F Heinrichs Springer-Verlag, 2024 | | 2024 |
| OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data S Wette, F Heinrichs arXiv preprint arXiv:2409.09742, 2024 | | 2024 |
| Algorithmen: Die Grundidee der systematischen Herangehensweise F Heinrichs Algorithmen, Zufall, Unsicherheit–und Pizza! Wie Mathematik uns hilft …, 2024 | | 2024 |
| Algorithmen und Zufall: Optimale Entscheidungen treffen trotz unsicherer Umstände F Heinrichs Algorithmen, Zufall, Unsicherheit–und Pizza! Wie Mathematik uns hilft …, 2024 | | 2024 |
| Optimierungsalgorithmen: Fundierte Entscheidungen treffen F Heinrichs Algorithmen, Zufall, Unsicherheit–und Pizza! Wie Mathematik uns hilft …, 2024 | | 2024 |
| Zufall und Wahrscheinlichkeiten: Unsicherheit modellieren F Heinrichs Algorithmen, Zufall, Unsicherheit–und Pizza! Wie Mathematik uns hilft …, 2024 | | 2024 |
| Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines M Varotto, F Heinrichs, T Schuerg, S Tomasin, S Valentin arXiv preprint arXiv:2405.09564, 2024 | | 2024 |
| Detecting changes in locally stationary time series F Heinrichs Dissertation, Bochum, Ruhr-Universität Bochum, 2020, 2020 | | 2020 |
| SUPPLEMENTARY MATERIAL ON “DETECTING DEVIATIONS FROM SECOND-ORDER STATIONARITY IN LOCALLY STATIONARY FUNCTIONAL TIME SERIES” A Bücher, H Dette, F Heinrichs | | 2019 |
| Algorithmen, Zufall, Unsicherheit–und Pizza! F Heinrichs | | |
| iscussion F Heinrichs, H Dette | | |
DetteProfessor für Mathematik在 rub.de 的电子邮件经过验证
DetteProfessor für Mathematik在 rub.de 的电子邮件经过验证
Axel BücherRuhr University Bochum, Germany在 rub.de 的电子邮件经过验证