Submitted Papers & Preprints

1. Hüllen, T., Witforth, S. and Kneib, T. (2025)
   Behind the Curtain: Technology Maturity Estimation Using Hidden Markov Models
2. Henrich, J., Pos, C., Zilke, M., Shiroya, P., Chanut, E., Yamchi, A. M., Yahyapour, R., Kneib, T. and Traulsen, I. (2025)
   Benchmarking pig detection and tracking under diverse and challenging conditions
3. Rosero, G., Schmidt, R., Kneib, T. and Brümmer, B. (2025)
   Semiparametric stochastic frontier analysis accounting for spatial dependency. A countrywide analysis of Ecuadorian cocoa producers
4. Bae, S., Westphal, C., Ammer, C., Hostert, P., Hüttel, S., Kis-Katos, K., Kneib, T., Kreft, H., Paul, C., Plieninger, T., Putzenlechner, B. M., Roetter, R., Schlund, M., Seidel, D., Siebert, S., Wiegand, K. and Knohl, A. (2025)
   Disentangling Landscape Heterogeneity: Compositional, Configurational, Vertical, and Temporal Heterogeneity by
5. Herp, M., Brachem, J., Altenbuchinger, M. and Kneib, T. (2025)
   Graphical Transformation Models
6. Costa, R., de Sousa, B., Kneib, T., Martins, R. and Mayr, A. (2025)
   Methodological guidance on clinical prediction models in mental health research
7. Gutiérrez-Botella, J., Armero, C., Kneib, T., Pata, M. P. and García-Seara, J. (2025)
   Bayesian competing risks survival modeling for assessing the cause of death of patients with heart failure
8. Hagemann, N., Kneib, T. and Möllenhoff, K. (2025)
   Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models
9. Hagemann, N., Guhl, D., Kneib, T., Möllenhoff, K. and Steiner, W. J. (2024)
   Dynamic Heterogeneity in Discrete Choice Experiments
10. Skevas, I. and Kneib, T. (2024)
    A copula-based semiparametric by-production stochastic frontier model
11. 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
12. 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
13. Brachem, J., Wiemann, P. F. V. and Kneib, T. (2024)
    Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
14. Thielmann, A., Kneib, T. and Säfken, B. (2023)
    Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection
15. Barna, D. M. Engeland, K., Kneib, T., Thorarinsdottir T. L. and Xu, C.-Y. (2023)
    Regional flood frequency analysis at multiple durations: a comparison of duration consistency in quantile and parameter regression techniques
16. Dupont, E., Marques, I. and Kneib, T. (2023)
    Demystifying Spatial Confounding
17. Kruse, R.-M., Säfken, B. and Kneib, T. (2023)
    Measuring Neural Complexity: A Covariance Penalty Approach
18. 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
19. Nadifar, M., Baghishani, H., Kneib, T. and Fallah, A. (2022)
    Flexible Bayesian modeling of counts: constructing penalized complexity priors
20. 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