Submitted Papers & Preprints

  1. Lado Baleato, Ó., Kneib, T., Cadarso-Suárez, C., Gude, F. (2022)
    Multivariate reference regions based on Multivariate Conditional Transformation Models. Application in the measurement of glycemic markers in diabetes
  2. Loske, D., Klumpp, M., Kneib, T. and Montemanni, R. (2022)
    Incorporating human factors in the Theory of Constraints: A data-driven throughput time evaluation for manual material handling in retail operations
  3. Riebl, H., Klein, N. and Kneib, T. (2021)
    Modeling Intra-Annual Tree Stem Growth with a Distributional Regression Approach for Gaussian Process Responses
  4. Friedrich, S., Groll, A., Ickstadt, K., Kneib, T., Pauly, M., Rahnenführer, J. and Friede, T. (2021)
    Regularization approaches in clinical biostatistics: A review of methods and their applications
  5. Säfken, B., Kneib, T. and Wood, S. (2021)
    On the degrees of freedom of the smoothing parameter
  6. Rügamer, D., Baumann, P., Kneib, T. and Hothorn, T. (2021)
    Transforming Autoregression: Interpretable and Expressive Time Series Forecasts
  7. Marmolejo-Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic, S., Kovanovic, V., González, J., Kneib, T., Bühlmann, P., Kook, L., Briseño-Sánchez, G., Ospina, R. (2021)
    Distributional regression analysis of learning analytics and educational data
  8. Correa, J. C., Kneib, T., Ospina, R. and Marmolejo-Ramos, F. (2021
    The Dunning-Kruger Effect: A Conceptual and Statistical Review
  9. Svanidze, D., Python, A.,Weisser, C., Säfken, B., Kneib, T. and Fu, J. (2021)
    Towards Replicable Deep Learning Algorithms in Public Health: A Case Study on Predicting Early COVID-19 Infections in China
  10. Dorn, F., Radice, R., Marra, G. and Kneib, T. (2021)
    A Bivariate Relative Poverty Line for Time and Income Poverty: Detecting Intersectional Differences Using Distributional Copulas
  11. Dorn, F., Maxand, S. and Kneib, T. (2021)
    The nonlinear dependence of income inequality and carbon emissions: potentials for a sustainable future
  12. Carlan, M., Kneib, T. and Klein, N. (2021)
    Bayesian Conditional Transformation Models
  13. Marques, I., Kneib, T. and Klein, N. (2020)
    A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes
  14. Wacker, B., Kneib, T. and Schlüter, J. (2020)
    On Existence and Uniqueness of Maximum Log-Likelihood Parameter Estimation for Two-Parameter Weibull Distributions
  15. Seebaß, J. V., Schlüter, J. C., Wacker, B. and Kneib, T. (2020)
    Application of an additive structured copula regression on the joint wind speed and wind direction distribution
  16. Wiemann, P., Kneib, T. and Hambuckers, J. (2019)
    Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond
  17. Gutleb, D. R., Roos, C., Heistermann, M., De Moor, D., Kneib, T., Noll, A., Schülke, O. and Ostner, J. (2019)
    A multi-locus genetic risk score modulates social buffering of HPA axis activity in wild male primates
  18. Herbst, H., Minnich, A., Herminghaus, S., Kneib, T., Wacker, B. and Schlüter, J. C. (2018)
    A Behavioral Economic Perspective on Demand Responsive Transportation
  19. Razen, A., Brunauer, W., Klein, N., Kneib, T., Lang, S. and Umlauf, N. (2015)
    Estimating Uncertainty in Real Estate Valuation: A Multilevel Approach based on Distributional and Quantile Regression
  20. Oelker, M. R., Sobotka, F., Klein, N. and Kneib, T. (2014)
    Semiparametric Mode Regression