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. Brachem, J., Wiemann, P. F. V. and Kneib, T. (2024)
   Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
6. Thielmann, A., Kneib, T. and Säfken, B. (2023)
   Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection
7. 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
8. Dupont, E., Marques, I. and Kneib, T. (2023)
   Demystifying Spatial Confounding
9. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
   Measuring Neural Complexity: A Covariance Penalty Approach
10. 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
11. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
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