Submitted Papers & Preprints

1. Dupont, E., Marques, I. and Kneib, T. (2023)
   Demystifying Spatial Confounding
2. 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
3. Urdangarin, A., Goicoa, T., Kneib, T., and Ugarte, M.D. (2023)
   A one-step spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
4. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
   Measuring Neural Complexity: A Covariance Penalty Approach
5. Rappl, A.,Carlan, M., Kneib, T., Klokman, S., and Bergherr, E. (2023)
   Bayesian Effect Selection in Structured Additive Quantile Regression
6. Reuter, A., Thielmann, A., Weisser, C., Säfken, B. and Kneib, T. (2023)
   Probabilistic Topic Modelling with Transformer Representations
7. 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
8. 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
9. Lichter, J., Wiemann, P. and Kneib, T. (2022)
   Variational Inference: Uncertainty Quantification in Additive Models
10. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
11. Axenbeck, J., Berner, A., and Kneib, T. (2022)
    What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity
12. 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
13. März, A., Klein, N., Kneib, T. and Mußhoff, O. (2022)
    Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression
14. 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
15. Säfken, B., Kneib, T. and Wood, S. (2021)
    On the degrees of freedom of the smoothing parameter
16. Dorn, F., Maxand, S. and Kneib, T. (2021)
    The nonlinear dependence of income inequality and carbon emissions: potentials for a sustainable future
17. Wiemann, P., Kneib, T. and Hambuckers, J. (2019)
    Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond