Submitted Papers & Preprints

1. Brachem, J., Wiemann, P. F. V. and Kneib, T. (2024)
   Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
2. Thielmann, A., Kneib, T. and Säfken, B. (2023)
   Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection
3. 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
4. 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
5. Dupont, E., Marques, I. and Kneib, T. (2023)
   Demystifying Spatial Confounding
6. 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
7. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
   Measuring Neural Complexity: A Covariance Penalty Approach
8. Reuter, A., Thielmann, A., Weisser, C., Säfken, B. and Kneib, T. (2023)
   Probabilistic Topic Modelling with Transformer Representations
9. 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
10. 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
11. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
12. Axenbeck, J., Berner, A., and Kneib, T. (2022)
    What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity
13. 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
14. März, A., Klein, N., Kneib, T. and Mußhoff, O. (2022)
    Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression
15. Klasen, S., Kneib, T., Lo Bue, M. C. and Prete V. (2022)
    What’s behind pro-poor growth? An investigation of its drivers and dynamics
16. Säfken, B., Kneib, T. and Wood, S. (2021)
    On the degrees of freedom of the smoothing parameter
17. Rappl, A.,Carlan, M., Kneib, T., Klokman, S., and Bergherr, E. (2022)
    Bayesian Effect Selection in Structured Additive Quantile Regression