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