| 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 | 1022 | 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 | 81 | 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 | 65 | 2014 |
| Rage against the mean–a review of distributional regression approaches T Kneib, A Silbersdorff, B Säfken Econometrics and Statistics, 2021 | 48 | 2021 |
| cAIC4: Conditional Akaike information criterion for lme4 B Saefken, D Ruegamer, T Kneib, S Greven R package version 0.3, 2018 | 23 | 2018 |
| 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 | 18 | 2021 |
| Gradient Boosting for Linear Mixed Models C Griesbach, B Säfken, E Waldmann The International Journal of Biostatistics, 2021 | 16 | 2021 |
| TTLocVis: A Twitter Topic Location Visualization Package G Kant, C Weisser, B Säfken Journal of Open Source Software 5 (54), 2507, 2020 | 14 | 2020 |
| 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 | 13 | 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 | 12 | 2020 |
| 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 | 9 | 2023 |
| Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean A Thielmann, RM Kruse, T Kneib, B Säfken arXiv preprint arXiv:2301.11862, 2023 | 4 | 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 | 4 | 2022 |
| Package ‘cAIC4’ B Saefken, D Ruegamer, P Baumann, RM Kruse | 4 | 2019 |
| Structural Neural Additive Models: Enhanced Interpretable Machine Learning M Luber, A Thielmann, B Säfken arXiv preprint arXiv:2302.09275, 2023 | 3 | 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 | 3 | 2023 |
| Model averaging for linear mixed models via augmented Lagrangian RM Kruse, A Silbersdorff, B Säfken Computational Statistics & Data Analysis 167, 107351, 2022 | 3 | 2022 |
| Conditional covariance penalties for mixed models B Säfken, T Kneib Scandinavian Journal of Statistics 47 (3), 990-1010, 2020 | 3 | 2020 |
| 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 | 2 | 2021 |
| Learning deep B Säfken, A Silbersdorff, C Weisser Universitätsverlag Göttingen, 2020 | 2 | 2020 |
Thomas KneibChair of Statistics, Georg-August-University GöttingenBestätigte E-Mail-Adresse bei uni-goettingen.de
