Submitted Papers & Preprints

  1. Wiemann, P. and Kneib, T. (2019)
    Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond
  2. Seiler, J., Kneib, T., Lang, S. and Harttgen, K.(2019)
    Modelling Children’s Anthropometric Status using Bayesian Distributional Regression merging Socio-economic and Remote Sensed Data from South Asia and Sub-Saharan Africa
  3. Röder, J., Muntermann, J. and Kneib, T. (2019)
    Towards a Taxonomy of Data Heterogeneity
  4. Voncken, L., Kneib, T., Albers, C. J., Umlauf, N. and Timmerman, M. E. (2019)
    Bayesian Gaussian distributional regression models for more efficient norm estimation
  5. Hohberg, M., Donat, F., Marra, G. and Kneib, T. (2019)
    Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes
  6. 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
  7. van der Wurp, H., Groll, A., Kneib, T., Marra, G. and Radice, R. (2019)
    Generalised joint regression for count data with a focus on modelling football matches
  8. Stadlmann, S. and Kneib, T. (2019)
    distreg.vis: Interactively visualizing distributional regression models
  9. Santos, B. and Kneib, T. (2019)
    Noncrossing structured additive multiple-output Bayesian quantile regression models
  10. Klein, N., Hothorn, T. and Kneib, T. (2019)
    Multivariate Conditional Transformation Models
  11. Hambuckers, J. and Kneib, T. (2019)
    Operational risk, uncertainty, and the economy: a smooth transition extreme value approach
  12. Briseño Sanchez, G., Hohberg, M., Groll, A. and Kneib, T. (2018)
    Flexible instrumental variable distributional regression
  13. Säfken, B. and Kneib, T. (2018)
    Conditional Covariance Penalties for Mixed Models
  14. Klein, N,, Carlan, M., Kneib, T., Lang, S. and Wagner, H. (2018)
    Bayesian Effect Selection in Structured Additive Distributional Regression Models
  15. Herbst, H., Minnich, A., Herminghaus, S., Kneib, T., Wacker, B. and Schlüter, J. C. (2018)
    A Behavioral Economic Perspective on Demand Responsive Transportation
  16. Udy, K. L., Fritsch, M., Grass, I., Hartig, F., Kneib, T., Kreft, H., Kukunda, C., Meyer, K. M., Pe’er, G., Reininghaus, H., Tietjen, B., Tscharntke, T., van Waveren, C.-S. and Wiegand, K. (2018)
    Environmental heterogeneity predicts global species richness patterns better than area
  17. Adam, T., Mayr, A. and Kneib, T. (2018)
    Gradient boosting in Markov-switching generalized additive models for location, scale and shape
  18. Hohberg, M., Pütz, P. and Kneib, T. (2017)
    Treatment Effects Beyond the Mean: A Practical Guide Using Distributional Regression
  19. Michaelis, P., Klein, N. and Kneib, T. (2017)
    Mixed Discrete-Continuous Regression - A Novel Approach Based on Weight Functions
  20. Mascarenhas, A., Marques, F., Silva, S., Gouveia, S., Alves, M., Virella, D., Papoila, A. L., Kneib, T., Klein, N. and Neto, M. T. (2017)
    Determinants of the Variability of Oxygen Saturation during the First Minutes of Life of Term Neonates
  21. Razen, A., Brunauer, W., Klein, N., Kneib, T., Lang, S. and Umlauf, N. (2015)
    Statistical risk analysis for real estate collateral valuation using Bayesian distributional and quantile regression
  22. Klein, N., Malzahn, D., Rosenberger, A., Lozano-Kühne, J., Kneib, T. and Bickeböller, H. (2015)
    Candidate gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios
  23. März, A., Klein, N., Kneib, T. and Mußhoff, O. (2015)
    Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression
  24. Oelker, M. R., Sobotka, F., Klein, N. and Kneib, T. (2014)
    Semiparametric Mode Regression
  25. Alvaro-Meca, A., Reulen, H., Kneib, T., Rodriguez-Gijon, L., Gil de Miguel, A. and Resino, S. (2013)
    Risk of hospital readmission and death among HIV infected patients with tuberculosis. A recurrent event analysis
  26. Sobotka, F., Schnabel, S., Schulze Waltrup, L., Kauermann, G. and Kneib, T. (2012)
    expectreg: An R Package for Expectile Regression