Submitted Papers & Preprints

1. 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
2. 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"
3. Brachem, J., Wiemann, P. F. V. and Kneib, T. (2024)
   Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
4. Thielmann, A., Kneib, T. and Säfken, B. (2023)
   Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection
5. 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
6. 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
7. Dupont, E., Marques, I. and Kneib, T. (2023)
   Demystifying Spatial Confounding
8. 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
9. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
   Measuring Neural Complexity: A Covariance Penalty Approach
10. Reuter, A., Thielmann, A., Weisser, C., Säfken, B. and Kneib, T. (2023)
    Probabilistic Topic Modelling with Transformer Representations
11. 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
12. 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
13. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
14. Axenbeck, J., Berner, A., and Kneib, T. (2022)
    What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity
15. 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
16. März, A., Klein, N., Kneib, T. and Mußhoff, O. (2022)
    Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression
17. Säfken, B., Kneib, T. and Wood, S. (2021)
    On the degrees of freedom of the smoothing parameter