| Smoothing parameter and model selection for general smooth models SN Wood, N Pya, B Säfken Journal of the American Statistical Association 111 (516), 1548-1563, 2016 | 1277 | 2016 |
| Conditional Model Selection in Mixed-Effects Models with cAIC4 B Säfken, D Rügamer, T Kneib, S Greven Journal of Statistical Software 99 (8), 2021 | 100 | 2021 |
| A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models B Saefken, T Kneib, CS van Waveren, S Greven Electronic Journal of Statistics 8 (1), 201-225, 2014 | 71 | 2014 |
| Rage against the mean–a review of distributional regression approaches T Kneib, A Silbersdorff, B Säfken Econometrics and Statistics 26, 99-123, 2023 | 70 | 2023 |
| Stock price predictions with LSTM neural networks and twitter sentiment ML Thormann, J Farchmin, C Weisser, RM Kruse, B Säfken, A Silbersdorff Statistics, Optimization & Information Computing 9 (2), 268-287, 2021 | 39 | 2021 |
| Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data C Weisser, C Gerloff, A Thielmann, A Python, A Reuter, T Kneib, B Säfken Computational statistics 38 (2), 647-674, 2023 | 26 | 2023 |
| Introductory data science across disciplines, using Python, case studies and industry consulting projects J Lasser, D Manik, A Silbersdorff, B Säfken, T Kneib Teaching Statistics, 2020 | 22 | 2020 |
| Gradient Boosting for Linear Mixed Models C Griesbach, B Säfken, E Waldmann The International Journal of Biostatistics, 2021 | 21 | 2021 |
| TTLocVis: A Twitter Topic Location Visualization Package G Kant, C Weisser, B Säfken Journal of Open Source Software 5 (54), 2507, 2020 | 21 | 2020 |
| cAIC4: Conditional Akaike information criterion for lme4 B Saefken, D Ruegamer, T Kneib, S Greven R package version 0.3, 2018 | 21 | 2018 |
| Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling A Thielmann, C Weisser, A Krenz, B Säfken Journal of Applied Statistics 50 (3), 574-591, 2023 | 19 | 2023 |
| Neural additive models for location scale and shape: A framework for interpretable neural regression beyond the mean AF Thielmann, RM Kruse, T Kneib, B Säfken International Conference on Artificial Intelligence and Statistics, 1783-1791, 2024 | 9 | 2024 |
| Coherence based document clustering A Thielmann, C Weisser, T Kneib, B Säfken 2023 IEEE 17th International Conference on Semantic Computing (ICSC), 9-16, 2023 | 8 | 2023 |
| An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter G Kant, L Wiebelt, C Weisser, K Kis-Katos, M Luber, B Säfken International Journal of Data Science and Analytics, 1-21, 2022 | 8 | 2022 |
| Structural neural additive models: Enhanced interpretable machine learning M Luber, A Thielmann, B Säfken arXiv preprint arXiv:2302.09275, 2023 | 7 | 2023 |
| Topics in the haystack: Enhancing topic quality through corpus expansion A Thielmann, A Reuter, Q Seifert, E Bergherr, B Säfken Computational Linguistics, 1-37, 2024 | 5 | 2024 |
| GPTopic: Dynamic and Interactive Topic Representations A Reuter, A Thielmann, C Weisser, S Fischer, B Säfken arXiv preprint arXiv:2403.03628, 2024 | 4 | 2024 |
| Model averaging for linear mixed models via augmented Lagrangian RM Kruse, A Silbersdorff, B Säfken Computational Statistics & Data Analysis 167, 107351, 2022 | 4 | 2022 |
| Community-detection via hashtag-graphs for semi-supervised NMF topic models M Luber, A Thielmann, C Weisser, B Säfken arXiv preprint arXiv:2111.10401, 2021 | 4 | 2021 |
| Identifying topical shifts in twitter streams: an integration of non-negative matrix factorisation, sentiment analysis and structural break models for large scale data M Luber, C Weisser, B Säfken, A Silbersdorff, T Kneib, K Kis-Katos Multidisciplinary International Symposium on Disinformation in Open Online …, 2021 | 4 | 2021 |
Thomas KneibChair of Statistics, Georg-August-University GöttingenBestätigte E-Mail-Adresse bei uni-goettingen.de
