Submitted Papers & Preprints

1. Hagemann, N., Guhl, D., Kneib, T., Möllenhoff, K. and Steiner, W. J. (2024)
   Dynamic Heterogeneity in Discrete Choice Experiments
2. Skevas, I. and Kneib, T. (2024)
   A copula-based semiparametric by-production stochastic frontier model
3. Schlee, M., Kant, G., Säfken, B. and Kneib, T. (2024)
   Decoding synthetic news: An interpretable multimodal framework for the classification of news articles in a novel news corpus
4. Bruns, S.B., Herwartz, H., Islam, C.G., Kneib, T. and Malina, R. (2024)
   Ambiguous empirical results are not oversold but more focused on statistical significance
5. Semnani, P., Bogojeski, M., Bley, F., Zhang, Z., Wu, Q., Kneib, T., Herrmann, J., Weisser, C., Patcas, F., Müller, K.-R. (2024)
   A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery"
6. Brachem, J., Wiemann, P. F. V. and Kneib, T. (2024)
   Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
7. Thielmann, A., Kneib, T. and Säfken, B. (2023)
   Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection
8. Kant, G., Zhelyazkov, I., Thielmann, A., Weisser, C., Schlee, M., Ehrling, C., Säfken, B. and Kneib, T. (2023)
   One-Way ticket to the Moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading
9. Barna, D. M. Engeland, K., Kneib, T., Thorarinsdottir T. L. and Xu, C.-Y. (2023)
   Regional index flood estimation at multiple durations with generalized additive models
10. Dupont, E., Marques, I. and Kneib, T. (2023)
    Demystifying Spatial Confounding
11. Henrich, J., van Delden, J., Seidel, D., Kneib, T and Ecker, A. (2023)
    TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Forest Point Clouds
12. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
    Measuring Neural Complexity: A Covariance Penalty Approach
13. Reuter, A., Thielmann, A., Weisser, C., Säfken, B. and Kneib, T. (2023)
    Probabilistic Topic Modelling with Transformer Representations
14. Thielmann, A., Kruse, R.-M., Kneib, T. and Säfken, B. (2023)
    Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
15. Michels, M., von Hobe, C.-F., Merk, M. S., Kneib, T. and Musshoff, O. (2022)
    The rent price ratio of individual decision-makers acting on the agricultural land market
16. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
17. Axenbeck, J., Berner, A., and Kneib, T. (2022)
    What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity
18. Riebl, H., Wiemann, P. F. V. and Kneib, T. (2022):
    Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms
19. März, A., Klein, N., Kneib, T. and Mußhoff, O. (2022)
    Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression
20. Säfken, B., Kneib, T. and Wood, S. (2021)
    On the degrees of freedom of the smoothing parameter