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Humboldt-Universität zu Berlin - Statistik

Prof. Dr. Nadja Klein

Applied Statistics


Mail address

Humboldt-University of Berlin
School of Business and Economics
Applied Statistics
Unter den Linden 6
10099 Berlin


Research Interests

  • Bayesian Statistics
  • Computational Methods
  • Machine Learning
  • Distributional Regression
  • Smoothing Methods
  • Copula Modelling
  • Shrinkage Priors and Variable Selection



Prizes and Awards

  • since 2016 Member of the Humboldt network (Alexander von Humboldt Foundation)
  • 07/2016–06/2018 Feodor-Lynen-Fellowship for Postdoctoral Researchers of the Alexander von Humboldt Foundation (hosted at the University of Melbourne; host: Michael Stanley Smith)
  • 2016 NSF-ISBA Junior Travel Support Grant of the US National Science Foundation
  • 2015 Wolfgang-Wetzel-Price 2015 of the German Statistical Society for the paper ‘Bayesian Generalized AdditiveModels for Location, Scale and Shape for Zero-Inflated and Overdispersed Count Data’
  • 2014 Award of the Georg-August-Universitaet Gorttingen for outstanding dissertation ‘Bayesian Structured Additive Distributional Regression’
  • 2014 Award of the Universitaetsbund Goettingen for the dissertation ‘Bayesian Structured Additive Distributional Regression’
  • 10/2013–10/2014 Awarded Membership in the Dorothea Schloezer Mentoring Programme, Georg-August-Universitaet Goettingen


Topics for Theses

  • Deep distributional learning
  • Uncertainty quantification for deep learning
  • Bayesian stacking approaches
  • Bayesian neural network structures
  • Deep Gaussian process modelling
  • Multiple Imputation for Panel Data
  • Using Stacking to Average Distributional Regression Models
  • Bivariate Distributional Regression for Wind Speed and Wind Direction
  • Modelling Income Inequality with Semiparametric Transformation Models
  • Bayesian Nonparametric Conditional Density Estimators
  • Distributional Joint Modelling
  • Probabilistic Weather Forecasts
  • Approximations of Normalizing Constants in Doubly-Intractable Likelihoods
  • Effect Fusion of Categorial Predictors
  • Effect Selection in Semiparametric Quantile Regression Models
  • Measuring the Explained Variance in Structured Additive Distributional Regression
  • Bayesian Hierarchical Modelling of Hedonic Housing Prices
  • Comparisons and Implementation of Non-Local Shrinkage Priors




  • Klein, N. (2015): Bayesian Structured Additive Distributional Regression. Dissertation.

Publications with peer review proces

  • Thaden, H., Klein, N., and Kneib, T. (2019): Multivariate E ect Priors in Semiparametric Recursive Bivariate Gaussian Models. To appear in Computational Statistics and Data Analysis.

  • Klein, N., and Smith, M. S. (2019): Implicit Copulas from Bayesian Regularized Regression Smoothers. To appear in Bayesian Analysis.

  • Kneib, T., Klein, N., Lang, S. and Umlauf, N. (2019): Modular Regression - A Lego System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions. To appear in TEST.

  • Pross, C., Strumann, C., Geissler, A., Herwartz, H. and Klein, N. (2018): Quality and resource efficiency in hospital service provision: A geoadditive stochastic frontier analysis of stroke quality of care in Germany. PLOS ONE, 13, 1-30.

  • Klein, N., Kneib, T., Marra, G., Radice, R., Rokicki, S. and McGovern, M. (2018): Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes. To appear in Statistics in Medicine.

  • Helbich, M., Klein, N., Roberts, H., Hagedoorn, P., Groenewegen, P. (2018): More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach. Environmental Research, 166, 290–297.

  • R´ıos-Pena, L., Kneib, T., Cadarso-Suarez, C., Klein, N. and Marey-P´erez, M. (2018) Studying the Occurrence and Burnt Area of Wildfires using Zero-One-Inflated Structured Additive Beta Regression Environmental Modelling and Software, 110, 107-118.

  • Umlauf, N., Klein, N., Zeileis, A. (2018): BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond). Journal of Computational and Graphical Statistics,  27, 612-627.

  • Michaelis, P., Klein, N. and Kneib, T. (2018): Bayesian Multivariate Distributional Regression with Skewed Responses and Skewed Random Effects. Journal of Computational and Graphical Statistics, 27, 602--611.

  • Duarte, E., de Sousa, B., Cadarso Surez, C., Klein, N., Kneib, T. and Rodrigues, V. (2017): Studying the relationship between a womans reproductive life span and age at menarche using a Bayesian multivariate structured additive distributional regression model. Biometrical Journal, 59, 1232–1246.

  • Thaden, H., Pata, M. P., Klein, N., Cadarso Suarez, C. and Kneib, T. (2017): Integrating Multivariate Conditionally Autoregressive Spatial Priors into Recursive Bivariate Models for Analyzing Environmen- tal Sensitivity of Mussels. Spatial Statistics, 22, 419–433.

  • Mamouridis, V., Klein, N., Kneib, T., Cadarso Suarez, C. and Maynou, F. (2017): Structured Ad- ditive Distributional Regression for Analyzing Landings per Unit Effort Indices in Fisheries Research. Mathematical Biosciences, 283, 145-154.

  • Waldmann, E., Taylor-Robinson D., Klein, N., Kneib T., Pressler T., Schmid, M. and Mayr, A.(2016): Boosting Joint Models for Longitudinal and Time-to-Event Data. Biometrical Journal, 59, 1104–1121.

  • Klein, N. and Kneib, T. (2016): Scale-Dependent Priors for Variance Parameters in Structured Ad- ditive Distributional Regression. Bayesian Analysis, 11, 1071–1106, https://projecteuclid.org/ euclid.ba/1448323525.

  • M¨arz, A., Klein, N., Kneib, T. and Muhoff, O. (2016): Analysing farmland rental rates using Bayesian geoadditive quantile regression. European Review of Agricultural Economics, 14, 663-698.

  • Klein, N. and Kneib, T. (2016): Simultaneous Inference in Structured Additive Conditional Copula Regression Models: A Unifying Bayesian Approach. Statistics and Computing, 26, 841–860, doi:10.1007/s11222-015-9573-6.

  • Kormann, U., Scherber, C., Tscharnke, T. Klein, N., Larbig, M, Valente, J., Hadley, A. and Betts, M. (2016): Corridors Restore Animal-Mediated Pollination in Fragmented Tropical Forest Landscapes. To appear in Proceedings of the Royal Society B. Early view available at http://rspb.royalsocietypublishing.org/content/283/1823/20152347.

  • Klein, N., Kneib, T. and Lang, S. (2015): Bayesian Generalized Additive Models for Location, Scale and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association,   110,    405–419,    http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2014.912955.

  • Klein, N., Kneib, T., Lang, S. and Sohn, A. (2015): Bayesian Structured Additive Distributional Regression with an Application to Regional Income Inequality in Germany. Annals of Applied Statistics, 9,    1024–1052,     http://www.imstat.org/aoas/next_issue.html.

  • Herwartz, H., Klein, N., and Strumann, C. (2015): Modelling Hospital Admission and Length of Stay by Means of Generalised Count Data Models. Journal of Applied Econometrics, 6, 1159-1182, doi: 10.1002/jae.2454.

  • Sohn, A., Klein, N. and Kneib, T. (2015): A Semiparametric Analysis of Conditional Income Dis- tributions. Schmollers Jahrbuch - Journal of Applied Science Studies, 135, 13–22, doi:10.3790/ schm.135.1.13.

  • Klein, N., Kneib, T., Klasen, S. and Lang, S. (2015): Bayesian Structured Additive Distributional Regression for Multivariate Responses. Journal of the Royal Statistical Society, Series C, 64, 569–591, http://onlinelibrary.wiley.com/doi/10.1111/rssc.12090/abstract.

  • Klein, N., Denuit, M., Lang, S. and Kneib, T. (2014): Nonlife Ratemaking and Risk Management with Bayesian Generalized Additive Models for Location, Scale, and Shape. Insurance: Mathematics and Economics, 55, 225–249.

  • Razen, A., Brunauer, W., Klein, N., Lang, S. and Umlauf, N. (2014): Hedonic House Price Modeling based on Multilevel Structured Additive Regression. In M. Helbich, J. Jokar, M. Leitner (eds), Com- putational Approaches for Urban Environments, Geotechnologies and the Environment, 13, 97–122, Springer Verlag.


  • Klein, N., Gude, F., Cadarso-Suarez, C. and Kneib, T. (2014): Bivariate Gaussian Distributional Regression: An Application on Diabetes. In T. Kneib, F. Sobotka, J. Fahrenholz, H. Irmer, (eds), Proceedings of the 29th International Workshop on Statistical Modelling, 1, 167–172.
  • Klein, N., Kneib, T. and Lang, S. (2013): Bayesian Generalized Additive Models for Location, Scale and Shape for Insurance Data. In V. M. R. Muggeo, V. Capursi, G. Boscaino and G. Lovison, (eds), Proceedings of the 28th International Workshop on Statistical Modelling, 2, 645–650.