| 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 | 17 | 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 | 8 | 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 | 6 | 2023 |
| Using crisp-dm to grow as data scientist F Heinrichs Towards Data Science. Acesso disponıvel em: https://towardsdatascience. com …, 2020 | 5 | 2020 |
| 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 | 3* | 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 | 1 | 2023 |
| GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis F Heinrichs arXiv preprint arXiv:2401.15623, 2024 | | 2024 |
| Monitoring Machine Learning Models: Online Detection of Relevant Deviations F Heinrichs arXiv preprint arXiv:2309.15187, 2023 | | 2023 |
| 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 |
| iscussion F Heinrichs, H Dette | | |
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