| Bayesian structured additive distributional regression with an application to regional income inequality in Germany N Klein, T Kneib, S Lang, A Sohn | 163 | 2015 |
| Six years ahead: a longitudinal analysis regarding course and predictive value of the Strengths and Difficulties Questionnaire (SDQ) in children and adolescents A Becker, A Rothenberger, A Sohn, BELLA Study Group European child & adolescent psychiatry 24, 715-725, 2015 | 98 | 2015 |
| Rage against the mean–a review of distributional regression approaches T Kneib, A Silbersdorff, B Säfken Econometrics and Statistics 26, 99-123, 2023 | 80 | 2023 |
| Nonparametric inference in hidden Markov models using P‐splines R Langrock, T Kneib, A Sohn, SL DeRuiter Biometrics 71 (2), 520-528, 2015 | 71 | 2015 |
| 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 |
| Reconsidering the income‐health relationship using distributional regression A Silbersdorff, J Lynch, S Klasen, T Kneib Health economics 27 (7), 1074-1088, 2018 | 25 | 2018 |
| 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 43, S190-S200, 2021 | 23 | 2021 |
| Firm-level energy rebound effects and relative efficiency in the German manufacturing sector A Berner, S Lange, A Silbersdorff Energy Economics 109, 105903, 2022 | 20 | 2022 |
| A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models J Hambuckers, T Kneib, R Langrock, A Silbersdorff Quantitative Finance 18 (10), 1679-1698, 2018 | 20 | 2018 |
| Semiparametric stochastic volatility modelling using penalized splines R Langrock, T Michelot, A Sohn, T Kneib Computational Statistics 30, 517-537, 2015 | 15 | 2015 |
| Economy-Wide Rebound Effects: State of the art, a new taxonomy, policy and research gaps S Lange, M Banning, A Berner, F Kern, C Lutz, J Peuckert, T Santarius, ... ReCap. Berlin (Arbeitsbericht 1), 2019 | 11 | 2019 |
| A semiparametric analysis of conditional income distributions A Sohn, N Klein, T Kneib Journal of Contextual Economics–Schmollers Jahrbuch, 13-22, 2015 | 11 | 2015 |
| Distributional regression techniques in socioeconomic research on the inequality of health with an application on the relationship between mental health and income A Silbersdorff, KS Schneider International Journal of Environmental Research and Public Health 16 (20), 4009, 2019 | 9 | 2019 |
| Rage against the mean: a review of distributional regression approaches. Econom Stat T Kneib, A Silbersdorff, B Säfken | 6 | 2021 |
| Rage against the mean–a review of distributional regression approaches, Econometrics and Statistics, 26, 99–123 T Kneib, A Silbersdorff, B Säfken | 5 | 2021 |
| A new semiparametric approach to analysing conditional income distributions A Sohn, N Klein, T Kneib Available at SSRN 2404335, 2014 | 5 | 2014 |
| Im historischen Sinkflug. Die Gehälter von Professoren A Sohn Zeitschrift für Forschung und Lehre 21 (9), 736-737, 2014 | 5 | 2014 |
| Model averaging for linear mixed models via augmented Lagrangian RM Kruse, A Silbersdorff, B Säfken Computational Statistics & Data Analysis 167, 107351, 2022 | 4 | 2022 |
| 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 |
| Analysing Inequalities in Germany: A Structured Additive Distributional Regression Approach A Silbersdorff Springer, 2017 | 4 | 2017 |
