| Smoothing parameter and model selection for general smooth models SN Wood, N Pya, B Säfken Journal of the American Statistical Association 111 (516), 1548-63, 2016 | 1396 | 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 | 116 | 2021 |
| Rage against the mean–a review of distributional regression approaches T Kneib, A Silbersdorff, B Säfken Econometrics and Statistics 26, 99-123, 2023 | 97 | 2023 |
| 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 | 74 | 2014 |
| 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 | 43 | 2021 |
| Gradient Boosting for Linear Mixed Models C Griesbach, B Säfken, E Waldmann The International Journal of Biostatistics, 2021 | 33 | 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 | 32 | 2023 |
| 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 | 29 | 2023 |
| Rejoinder - Smoothing parameter and model selection for general smooth models SN Wood, N Pya, S B. Journal of the American Statistical Association 111 (516), 1548-63, 2016 | 29* | 2016 |
| 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 | 24 | 2020 |
| TTLocVis: A Twitter Topic Location Visualization Package G Kant, C Weisser, B Säfken Journal of Open Source Software 5 (54), 2507, 2020 | 23 | 2020 |
| cAIC4: Conditional Akaike information criterion for lme4 B Saefken, D Ruegamer, T Kneib, S Greven R package version 0.3, 2018 | 21 | 2018 |
| 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 | 15 | 2024 |
| cAIC4: conditional Akaike information criterion for lme4, R package version 0.3 B Saefken, D Ruegamer, T Kneib, S Greven Https//Cran. r-Project. Org/Package=, 2018 | 15* | 2018 |
| 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 | 13* | 2024 |
| Structural neural additive models: Enhanced interpretable machine learning M Luber, A Thielmann, B Säfken arXiv preprint arXiv:2302.09275, 2023 | 11 | 2023 |
| Coherence based document clustering A Thielmann, C Weisser, T Kneib, B Säfken 2023 IEEE 17th International Conference on Semantic Computing (ICSC), 9-16, 2023 | 11 | 2023 |
| Rage against the mean–a review of distributional regression approaches, Econometrics and Statistics, 26, 99–123 T Kneib, A Silbersdorff, B Säfken | 10 | 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 | 10 | 2022 |
| Mambular: A sequential model for tabular deep learning AF Thielmann, M Kumar, C Weisser, A Reuter, B Säfken, S Samiee arXiv preprint arXiv:2408.06291, 2024 | 5 | 2024 |
Thomas KneibChair of Statistics, Georg-August-University Göttingenכתובת אימייל מאומתת בדומיין uni-goettingen.de
