Published Books
Biagini, G., Kauermann, G., Meyer-Brandis, T. (2019). Network Science. Springer Publishing.
Kauermann, G., Küchenhoff, H. (2011). Samples. Springer Verlag.
Published Papers
Gruber, C.; Alber, H.; Bischl, B.; Kauermann, G.; Plank, B. and Aßenmacher, M. (2025). Revisiting Active Learning under (Human) Label Variation. NLPPerspectives2025 - Conference Proceedings (to appear).
Hechinger, K.; Schweden, C.; Zhu, X. and Kauermann, G. (2025). Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty. Statistical Modelling (to appear).
Gruber, C.; Schenk, P.O.; Schierholz, M.; Kreuter, F. and Kauermann, G. (2025). Sources of Uncertainty in Supervised Machine Learning - A Statisticians' View. Statistical Science (to appear).
Racek, D., Thurner, P. and Kauermann, G. (2025). Capturing the Spatio-Temporal Diffusion Effects of Armed Conflict: A Non-parametric Smoothing Approach. Journal of the Royal Statistical Society, Series A (to appear).
Schweden, C., Hechinger, K., Kauermann, G. and Zhu X.X. (2025). Can Uncertainty Quantification Benefit From Label Embeddings? A Case Study on Local Climate Zone Classification IEEE Transactions on Geoscience and Remote Sensing. Vol 63, 1 - 14.
Berger, U., Kauermann, G. and Küchenhoff, H. (2025). "Contribution to the Discussion of 'Statistical aspects of the Covid-19 response' by Wood et al.". Journal of the Royal Stat. Society, Series A. (to appear).
Kirch, C., Lahiri, S., Binder, H., Brannath, W., Cribben, I., Dette, H., Doebler, P., Feng, O., Gandy, A., Greven, S., Hammer, B., Harmeling, S., Hotz, T., Kauermann, G., Krause, J., Krempl, G., Nieto-Reyes, A., Okhrin, O., Ombao, H., Pein, F., Pešta, M., Politis, D., Qin. L.X., Rainforth, T., Rauhut, H., Reeve, H., Salinas, D., Schmidt-Hieber, J., Scott, C., Segers, J., Spiliopoulou, M., Wilhelm, A., Wilms, I., Yu, Y. and Lederer, J. (2025). Challenges and Opportunities for Statistics in the Era of Data Science. Harvard Data Science Review. (to appear).
Windmann, M., Reichert, A., Fürnrohr, M., Kauermann, G. (2025). Mietwohnungen in München – Ein Vergleich von Zensusdaten und Mietspiegeldaten, AStA Wirtschafts- und Sozialstatistisches Archiv. https://doi.org/10.1007/s11943-025-00353-9 (2025).
Sischka, B. and Kauermann, G. (2025). Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation. Network Science (to appear).
Schneble, Marc and Kauermann, Göran (2025). Statistical modeling of on-street parking spot occupancy in smart cities. Journal of the Royal Statistical Society, Series C (to appear).
Lebacher, M., & Kauermann, G. (2024). Regression‐based network‐flow and inner‐matrix reconstruction. Scandinavian Journal of Statistics, 51, 1730-1748.
De Nicola, G., & Kauermann, G. (2024). Estimating excess mortality in high-income countries during the COVID-19 pandemic. Journal of the Royal Statistical Society Series A: Statistics in Society, qnae031.
Fritz, C., Mehrl, M., & Thurner, P. W. (2022). Exponential random graph models for dynamic signed networks: An application to international relations. arXiv preprint arXiv:2205.13411.
Rave, M., & Kauermann, G. (2023). The Skellam distribution revisited: Estimating the unobserved incoming and outgoing ICU COVID-19 patients on a regional level in Germany. Statistical Modelling, 1471082X241235024.
Meyer, F., Kauermann, G., Alder, C., & Cleophas, C. (2024). Modeling price-sensitive demand in turbulent times: an application to continuous pricing. Journal of Revenue and Pricing Management, 1-25.
Fritz, C., De Nicola, G., Rave, M., Weigert, M., Khazaei, Y., Berger, U., ... & Kauermann, G. (2024). Statistical modelling of COVID-19 data: Putting generalized additive models to work. Statistical Modelling, 24(4), 344-367.
Striegel, C., Biehler, J., & Kauermann, G. (2024). Weighted high dimensional data reduction of finite element features: an application on high pressure of an abdominal aortic aneurysm. Computational Statistics, 39(5), 2771-2789.
Hechinger, K., Zhu, X. X., & Kauermann, G. (2024). Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models. Journal of the Royal Statistical Society Series C: Applied Statistics, 73(1), 143-161.
Racek, D., Davidson, B. I., Thurner, P. W., Zhu, X. X., & Kauermann, G. (2024). The Russian war in Ukraine increased Ukrainian language use on social media. Communications Psychology, 2(1), 1.
Koller, C., Kauermann, G., & Zhu, X. X. (2023). Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance in Earth Observation? IEEE Transactions on Geoscience and Remote Sensing.
Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2023). All that glitters is not gold: Relational events models with spurious events. Network Science, 11(2), 184-204.
Meyer, J. F., Kauermann, G. R., & Smith, M. S. (2024). Interpretable modelling of retail demand and price elasticity for passenger flights using booking data. Statistical Modelling, 24(1), 82-106.
De Nicola, G., Kauermann, G., & Höhle, M. (2022). On assessing excess mortality in Germany during the COVID-19 pandemic. AStA Wirtschafts-und Sozialstatistisches Archiv, 16(1), 5-20.
Schneble, M., & Kauermann, G. (2022). Intensity estimation on geometric networks with penalized splines. The Annals of Applied Statistics, 16(2), 843-865.
De Nicola, G., Fritz, C., Mehrl, M., & Kauermann, G. (2023). Dependence matters: Statistical models to identify the drivers of tie formation in economic networks. Journal of Economic Behavior & Organization, 215, 351-363.
Hechinger, K., Zhu, X. X., & Kauermann, G. (2024). Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models. Journal of the Royal Statistical Society Series C: Applied Statistics, 73(1), 143-161.
De Nicola, G., Tuekam Mambou, V. H., & Kauermann, G. (2023). COVID-19 and social media: Beyond polarization. PNAS nexus, 2(8), pgad246.
Striegel, C., Biehler, J., & Kauermann, G. (2024). Weighted high dimensional data reduction of finite element features: an application on high pressure of an abdominal aortic aneurysm. Computational Statistics, 39(5), 2771-2789.
Kauermann, G., & Windmann, M. (2023). Die Berücksichtigung von außergesetzlichen Merkmalen bei der Mietspiegelerstellung–Kausalität versus Vorhersage. AStA Wirtschafts-und Sozialstatistisches Archiv, 17(2), 145-160.
Racek, D., Thurner, P. W., Davidson, B. I., Zhu, X. X., & Kauermann, G. (2024). Conflict forecasting using remote sensing data: An application to the Syrian civil war. International Journal of Forecasting, 40(1), 373-391.
Kauermann, G. and De Nicola, G. (2023). Übersterblichkeit durch Corona? WISTA-Wirtschafts- und Sozialstatistik. Ausgabe 1-2023
Fritz, C., De Nicola, G., Kevork, S., Harhoff, D., & Kauermann, G. (2023). Modelling the large and dynamically growing bipartite network of German patents and inventors. Journal of the Royal Statistical Society Series A: Statistics in Society, 186(3), 557-576.
Fahrmeir, L., Kauermann, G., Tutz, G., & Windmann, M. (2023). Spatial smoothing revisited: An application to rental data in Munich. Statistical Modelling, 23(5-6), 480-494.
Huang, Y. W., Prehofer, C., Lindskog, W., Puts, R., Mosca, P., & Kauermann, G. (2022, December). Predictive energy management for battery electric vehicles with hybrid models. In International Conference on Intelligent Transport Systems (pp. 182-196). Cham: Springer Nature Switzerland.
Fritz, C., De Nicola, G., Rave, M., Weigert, M., Khazaei, Y., Berger, U., ... & Kauermann, G. (2024). Statistical modelling of COVID-19 data: Putting generalized additive models to work. Statistical Modelling, 24(4), 344-367, https://doi.org/10.1177/1471082X221124628 (online first).
Fritz, C., De Nicola, G., Günther, F., Rügamer, D., Rave, M., Schneble, M., ... & Kauermann, G. (2023). Challenges in interpreting epidemiological surveillance data–experiences from Germany. Journal of Computational and Graphical Statistics, 32(3), 765-766.
Kevork, S., & Kauermann, G. (2022). Bipartite exponential random graph models with nodal random effects. Social Networks, 70, 90-99.
De Nicola, G., Schneble, M., Kauermann, G. and Berger, U. (2022). Regional now- and forecasting for data reported with delay: Towards surveillance of COVID-19 infections. AStA Advances in Statistical Analysis. 106, 407 – 426
Berger, U., Kauermann, G., & Küchenhoff, H. (2022). Discussion on On the role of data, statistics and decisions in a pandemic: by Jahn et al (2022). AStA Advances in Statistical Analysis, 106(3), 387-390.
Berger, U., Fritz, C. and Kauermann, G. (2022). Reihentestungen an Schulen können die Dunkelziffer von COVID-19 Infektionen unter Schülern signifikant senken. Das Gesundheitswesen. 84(06): 495-502
De Nicola, G. and Kauermann, G. (2022). An update on excess mortality in the second year of the COVID-19 pandemic in Germany. (Letter to the Editor) AStA Wirtschafts- und Sozialstatistisches Archiv, 16, 21 – 24.
De Nicola, G., Kauermann, G. and Höhle, M. (2022): On assessing excess mortality in Germany during the COVID-19 pandemic. AStA Wirtschafts- und Sozialstatistisches Archiv, 16, 5 - 20
Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2022). The role of governmental weapons procurements in forecasting monthly fatalities in intrastate conflicts: A semiparametric hierarchical hurdle model. International Interactions, 48(4), 778-799.
Meyer, J. F., Kauermann, G. R., & Smith, M. S. (2024). Interpretable modelling of retail demand and price elasticity for passenger flights using booking data. Statistical Modelling, 24(1), 82-106.
Schneble, M. and Kauermann, G. (2022) Intensity Estimation on Geometric Networks with Penalized Splines. Annals of Applied Statistics, 16.2 (2022): 843-865
Striegel, C., Biehler, J., Wall, W. A., & Kauermann, G. (2022). A multifidelity function-on-function model applied to an abdominal aortic aneurysm. Technometrics, 64(3),279-290, https://doi.org/10.1080/00401706.2021.2024453
Fritz, C. and Kauermann, G. (2022). On the interplay of regional mobility, social connectedness and the spread of COVID‐19 in Germany. Journal of the Royal Statistical Society, Series A, 185(1), 400-424.
Bauer, V., Harhoff, D., & Kauermann, G. (2021). A smooth dynamic network model for patent collaboration data. AStA advances in statistical analysis, 1-20, https://doi.org/10.1007/s10182-021-00393-w
Kevork, S., & Kauermann, G. (2022). Bipartite exponential random graph models with nodal random effects. Social Networks, 70, 90-99, https://doi.org/10.1016/j.socnet.2021.11.002.
Kevork, S., & Kauermann, G. (2021). Iterative estimation of mixed exponential random graph models with nodal random effects. Network Science, 9(4), 478-498, https://doi.org/10.1017/nws.2021.22
Sischka, B. and Kauermann, G. (2022) EM-Based Smooth Graphon Estimation Using MCMC and Spline-Based Approaches. Social Networks, 68, 279 – 295.
Fritz, C., Thurner, P.W. and Kauermann, G. (2021) Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades. Network Science, 9(3), 291 - 311.
De Nicola, G., Sischka, B., & Kauermann, G. (2022). Mixture models and networks: The stochastic blockmodel. Statistical Modelling, 22(1-2), 67-94.
Schneble, M., De Nicola, G., Kauermann, G., & Berger, U. (2021). A statistical model for the dynamics of COVID‐19 infections and their case detection ratio in 2020. Biometrical Journal, 63(8),1623-1632, https://doi.org/10.1002/bimj.202100125
Lebacher, M., Thurner, P. W., & Kauermann, G. (2021). Censored regression for modelling small arms trade volumes and its ‘Forensic’use for exploring unreported trades. Journal of the Royal Statistical Society Series C: Applied Statistics, 70(4), 909-933, https://doi.org/10.1111/rssc.12491
Ali, M. and Kauermann, G. (2021) A Split Questionnaire Survey Design in the Context of Statistical Matching. Journal of Statistical Methods and Applications, 30, 1219 – 1236.
Carballo, A., Durban, M., Kauermann, G., & Lee, D. J. (2021). A general framework for prediction in penalized regression. Statistical Modelling, 21(4), 293-312, doi:10.1177/1471082X19896867
Kauermann, G., & Ali, M. (2021). Semi-parametric regression when some (expensive) covariates are missing by design. Statistical Papers, 62(4), 1675-1696, doi:10.1007/s00362-019-01152-5
Schneble, M., De Nicola, G., Kauermann, G., & Berger, U. (2021). Nowcasting fatal COVID‐19 infections on a regional level in Germany. Biometrical Journal, 63(3), 471-489, https://doi.org/10.1002/bimj.202000143
Lebacher, M., Thurner, P. und Kauermann, G. (2021) A Dynamic Separable Network Model with Actor Heterogeneity: An Application to Global Weapons Transfers, Journal of the Royal Statistical Society, Series A, 184(1), 201 – 226.
Schneble, M., & Kauermann, G. (2022). Estimation of latent network flows in bike-sharing systems. Statistical Modelling, 22(4), 349-378, https://doi.org/10.1177/1471082X20971911
Lebacher, M., Thurner, P. W., & Kauermann, G. (2020). Exploring dependence structures in the international arms trade network: a network autocorrelation approach. Statistical Modelling, 20(2), 195-218, doi:10.1177/1471082X18817673
Fritz, C., Lebacher, M., & Kauermann, G. (2020). Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time. Statistica Neerlandica, 74(3), 275-299, doi:10.1111/stan.12198
Beyer, A., Kauermann, G., & Schütze, H. (2020, May). Embedding space correlation as a measure of domain similarity. In Proceedings of the Twelfth Language Resources and Evaluation Conference (pp.2431-2439), https://www.aclweb.org/anthology/2020.lrec-1.296/
Kauermann, G.; Windmann, M.; Münnich, R. (2020) Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik, AStA Wirtschafts- und Sozialstatistisches Archiv, 14, 145 - 162.
Shao, S., Kauermann, G. and Smith, M.S. (2020) Whether, when and which: modelling advanced seat reservations by airline passengers. Transportation Research Part A, 132, 490-514.
Kauermann, G. (2019) Data Science - aus Sicht eines Statistikers.
Informatik Spektrum. 42, 387–393.
Lebacher, M., Cook, S., Klein, N. and Kauermann, G. (2019) In Search of Lost Edges: A Case Study on Reconstructing Financial Networks. Journal of Network Theory in Finance Journal of Network Theory in Finance, 5(4), 29 – 61.
Bauer, V., Fürlinger, K. and Kauermann, G. (2019) A Note on Parallel Sampling in Markov Graphs. Computational Statistics, 34, 1087–1107.
Shao, S. und Kauermann, G. (2019) Understanding price elasticity for airline ancillary services. Journal of Revenue and Pricing Management, 19, 74–82.
Thurner, P. W., Schmid, C. S., Cranmer, S. J., & Kauermann, G. (2019). Network interdependencies and the evolution of the international arms trade. Journal of Conflict Resolution, 63(7), 1736-1764, doi:10.1177/0022002718801965
Kauermann, G. und Seidl, T. (2018) Data Science - A proposal for a Curriculum.
International Journal of Data Science and Analytics. 6, 195–199.
Bothmann, L., Menzel, A., Menze, B. H., Schunk, C., & Kauermann, G. (2017). Automated processing of webcam images for phenological classification. PloS one, 12(2), e0171918.
Kauermann, G., Becher, H., & Maier, V. (2018). Exploring the statistical uncertainty in acceptable exposure limit values for hexavalent chromium exposure. Journal of Exposure Science & Environmental Epidemiology, 28(1), 69-75.
Schulze Waltrup, L. and Kauermann, G. (2017) Smooth Expectiles for Panel Data using Penalized Splines. Statistics and Computing 27(1): 271-282.
Thiemichen, S. and Kauermann, G. (2017) Stable exponential random graph models with non-parametric components for large dense networks. Social Networks, 49, 67-80.
Bothmann, L., Windmann, M. and Kauermann, G. (2016) Realtime Classification of Fish in Underwater Sonar Videos. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(4), 565-584.
Brenner, T., & Kauermann, G. (2016). Specialization and convergence of industry-specific employment in Germany: A linear mixed-model approach with spatial components. Regional Studies, 50(2), 326-341, doi:10.1080/00343404.2014.920082
Bruns, P., Paschedag, H. und Kauermann, G. (2016): Anerkannte wissenschaftliche Grundsätze zur Erstellung qualifizierter Mietspiegel. Zeitschrift für Miet- und Raumrecht, 69, 669 – 679.
Kauermann, G., Küchenhoff, H. (2016): Statistik, Data Science und Big Data. AStA Wirtschafts- und Sozialstatistisches Archiv, 10, 141-150.
Kauermann, G. and Windmann, M. (2016) Mietspiegel heute. Zwischen Realität und statistischen Möglichkeiten. AStA Wirtschafts - und Sozialstatistisches Archiv, 10(4), 205-223.
Schulze Waltrup, L. and Kauermann, G. (2016) A Short Note on Quantile and Expectile Estimation in Unequal Probability Samples. Survey Methodology, 42(1), 179-187.
Thiemichen, S., Friel, N., Caimo, A., & Kauermann, G. (2016). Bayesian exponential random graph models with nodal random effects. Social Networks, 46, 11-28, doi:10.1016/j.socnet.2016.01.002
Wegener, M. and Kauermann, G. (2015) Forecasting in Nonlinear Univariate Time Series using Penalized Splines. Statistical Papers, 8(3), 557-576.
Kauermann, G. and Windmann, M. (2015): Quo vadis qualifizierter Meitspiegel - Anforderungen, Herausforderungen und Schwachstellen aus Sicht der Statistik. Grundstücksmarkt und Grundstückswert (6), 321-326.
Schulze Waltrup, L., Sobotka, F., Kneib, T. and Kauermann, G. (2014) Expectile and Quantile Regression - David and Goliath? Statistical Modelling, 15(5), 433-456.
Kauermann, G. and Schellhase, C. (2014) Flexible Pair-Copula Estimation in D-vines with Penalized Splines. Statistics and Computing, 24(6), 1081-1100.
Sabanés Bové, D., Held, L. and Kauermann, G. (2014) Objective Bayesian Model Selection in Generalised Additive Models with Penalised Splines. Journal of Computational and Graphical Statistics, 24(2), 394-415.
Brenner, T. and Kauermann, G. (2014) Specialisation and Convergence of Industry-Specific Employment in Germany - A Linear Mixed Model Approach with Spatial Components. Regional Studies, 50(2), 326-341.
Kauermann, G. and Meyer, R. (2014) Penalized Marginal Likelihood Estimation of Finite Mixtures of Archimedean Copulas. Computational Statistics, 29, 283-306.
Kauermann, G., Schellhase, C. and Ruppert, D. (2013) Flexible Copula Density Estimation with Penalized Hierarchical B-Splines. Scandinavian Journal of Statistics 40(4), 685-703.
Sobotka, F., Kauermann, G., Schulze Waltrup, L. and Kneib, T. (2013) On Confidence Intervals for Geoadditive Expectile Regression Models. Statistics and Computing, 23(2), 135-148.
Mestekemper, T., Kauermann, G. and Smith, M. (2013) A Comparison of Periodic Autoregressive and Dynamic Factor Models in Intraday Energy Demand Forecasting. International Journal of Forecasting, 29(1), 1-12.
Kauermann, G., Haupt, H. and Kaufmann, N. (2012) A Hitchhiker's View on Spatial Statistics and Spatial Econometrics for Lattice Data. Statistical Modelling, 12(5), 419-440.
Kauermann, G. and Westerheide, N. (2012) To move or not to move to find a new job - Spatial Duration Time Model with Dynamic Covariate Effects. Journal of Applied Statistics, 39(5), 995-1009.
Schellhase, C. and Kauermann, G. (2012) Density Estimation and Comparison with a Penalized Mixture Approach. Computational Statistics, 27(4), 757-777.
Westerheide, N., & Kauermann, G. (2012). Flexible modelling of duration of unemployment using functional hazard models and penalized splines: A case study comparing Germany and the UK. Studies in Nonlinear Dynamics & Econometrics, 16(1).
Kauermann, G., Teuber, T. and Flaschel, P. (2012) Exploring US Business Cycles with Bivariate Loops using Penalized Spline Regression. Computational Economics, 39, 409-427.
Kauermann, G. and Mestekemper, T. (2012) A short note on quantifying and visualizing yearly variation in online monitored temperature data. Statistical Modelling, 12, 195-209.
Smith, M. and Kauermann, G. (2011) Bicycle Commuting in Melbourne during the 2000s Energy Crisis: A Semiparametric Analysis of Intraday Volumes. Transportation Research Part B: Methodological, 45, 1846-1862.
Kauermann, G., Krivobokova, T., & Semmler, W. (2011). Filtering time series with penalized splines. Studies in Nonlinear Dynamics & Econometrics, 15(2).
Kauermann, G. and Opsomer, J.D. (2011) Data-driven Selection of the Spline Dimension in Penalized Spline Regression. Biometrika, 98(1), 225-230.
Kauermann, G. and Wegener, M. (2011) Functional Variance Estimation using Penalized Splines with Principal Component Analysis. Statistics and Computing, 21, 159-172.
Kuhlenkasper, T. and Kauermann, G. (2010) Duration of maternity leave in Germany: A case study of nonparametric hazard models and penalized splines. Labour Economics, 17(3), 466-473.
Mestekemper, T., Windmann, M. and Kauermann, G. (2010) Functional Hourly Forecasting of Water Temperature. International Journal of Forecasting, 26(4), 684-699.
Kauermann, G., Ormerod, J. and Wand, M.P. (2010) Parsimonious Classification via Generalized Linear Mixed Models. Journal of Classification, 27(1), 89-110.
Mikolajczyk, R. T., Kauermann, G., Sagel, U. and Kretschmar, M. (2009) A Mixture Model to Assess the Extent of Cross-Transmission of Multi-Resistant Pathogens in Hospitals. Infection Control and Hospital Epidemiology, 30(8), 730-736.
Becher, H., Kauermann, G., Khomski, P. and Kouyate, B. (2009) Using penalized splines to model age- and season-of-birthdependent effects of childhood mortality risk factors in rural Burkina Faso. Biometrical Journal, 51(1), 110-122.
Kauermann, G. and Khomski, P. (2009) Full Time or Part Time Reemployment: A Competing Risk Model with Frailties and Smooth Effects using a Penalty based Approach. Journal of Computational and Graphical Statistics, 18(1), 106-125.
Kauermann, G., Claeskens, G. and Opsomer, J. D. (2009): Bootstrapping for Penalized Spline Regression. Journal of Computational and Graphical Statistics, 18(1), 126-146.
Kauermann, G., Krivobokova, T. and Fahrmeir, L. (2009) Some Asymptotic Results on Generalized Penalized Spline Smoothing. Journal of the Royal Statistical Society, Series B, 71(2), 487-503.
Krivobokova, T., Crainiceanu, C.M. and Kauermann, G. (2008) Fast Adaptive Penalized Splines. Journal of Computational and Graphical Statistics, 17(1), 1-20.
Greiner, A., Kauermann, G. (2008): Dept policy in Euro-area countries: Evidence for Germany and Italy using penalized spline smoothing. Economic Modelling, 25 (6), 1144-1154.
Wegener, M. and Kauermann, G. (2008) Modelling Equity Risk Premium using Penalized Splines. Advances in Statistical Analysis, 92, 35-56.
Kauermann, G., Xu, R. and Vaida, F. (2008) Stacked Laplace-EM Algorithm for Duration Models with Time-Varying and Random Effects. Computational Statistics and Data Analysis, 52, 2514-2528.
Opsomer, J.D., Claeskens, G., Ranalli, G., Kauermann, G. and Breidt, F.J. (2008) Nonparametric small area estimation using penalized spline regression. Journal of the Royal Statistical Society, Series B, 70, 265-286.
Flaschel, P., Kauermann, G. and Semmler, W. (2007) Testing Wage and Price Phillips Curves for the United States. Metroeconomica, 58(4), 550-581.
Eisenbeiß, M., Kauermann, G., & Semmler, W. (2007). Estimating beta-coefficients of German stock data: A non-parametric approach. The European Journal of Finance, 13(6), 503-522.
Windmann, M. and Kauermann, G. (2007) Statistical Consulting at German Universities - Results of a Survey. Advances in Statistical Analysis, 91, 367-378.
Krivobokova, T. and Kauermann, G. (2007) A Note on Penalized Spline Smoothing with Correlated Errors. Journal of the American Statistical Association, 102, 1328-1337.
Wager, C., Vaida, F. and Kauermann, G. (2007) Model Selection for P-Spline Smoothing using Akaike Information Criteria. Australian and New Zealand Journal of Statistics, 49(2), 173-190.
Greiner, A., Kauermann, G. (2007) Sustainability of US public debt: Estimating smoothing spline regression. Economic Modelling, 24, 250-364.
Brown, D., Kauermann, G. and Ford, I. (2007) A partial likelihood approach to the smooth estimation of dynamic covariate effects. Biometrical Journal, 49, 441-452.
J.D. Opsomer, F.J. Breidt, G.G. Moisen and G. Kauermann (2007) Model-assisted estimation of forest resources with generalized additive models (with discussion). Journal of the American Statistical Association,102, 400-416.
Kauermann, G. and Khomski, P. (2006) Additive Two Way Hazards Model with Varying Coefficients. Computational Statistics and Data Analysis, 51 (3), 1944-1956.
Kauermann, G. (2006) Nonparametric models and their estimation. Allgemeines Statistisches Archiv, 90, 135-150.
Krivobokova, T., Kauermann, G. and Archontakis, T. (2006) Estimating the term structure of interest rates using penalized splines. Statistical Papers, 47(3), 443-459.
Flaschel, P., Kauermann, G., & Teuber, T. (2005). Long Cycles in Employment, Inflation and Real Unit Wage Costs Qualitative Analysis and Quantitative Assessment. American Journal of Applied Sciences, 2(13), 69-77.
Kauermann, G., Tutz, G. and Brüderl, J. (2005) The survival of newly founded firms: A case study into varying-coefficient models. Journal of the Royal Statistical Society, Series A, 168, 145-158.
Kauermann, G. (2005) Penalised Spline Fitting in Multivariable Survival Models with Varying Coefficients. Computational Statistics and Data Analysis, 49, 169-186.
Kauermann, G. (2005) A note on smoothing parameter selection for penalised spline smoothing. Journal of Statistical Planing and Inference, 127, 53-69.
Kauermann, G. and Eilers, P. (2004) Modelling microarray data using a threshold mixture model. Biometrics, 60, 376-387.
Kauermann, G. and Opsomer, J. (2004) Generalized Cross-validation for Bandwidth Selection of Backfitting Estimates in Generalized Additive Models. Journal of Computational and Graphical Statistics, 13, 66-89.
Kauermann, G. and Ortlieb, R. (2004) Temporal pattern in the number of staff on sick leave: The effect of downsizing. Journal of the Royal Statistical Society, Series C, 53, 353-367.
Kauermann, G. and Berger, U. (2003) A smooth test in proportional hazard models using local partial likelihood fitting. Lifetime Data Analysis, 9, 373-393.
Einbeck, J. and Kauermann, G. (2003) Online Monitoring with Local Smoothing Methods and Adaptive Ridging. Journal of Statistical Computation and Simulation, 73(12), 913-929.
Kauermann, G. and Opsomer, J. (2003) Local likelihood estimation in Generalized Additive Models. Scandinavian Journal of Statistics, 30, 317-337.
Tutz, G. and Kauermann, G. (2003): Generalized linear random effect models with varying coefficients. Computational Statistics and Data Analysis, 43, 13-28.
Kauermann, G. and Küchenhoff, H. (2003) Modelling Data from Inside of Earth: Local Smoothing of Mean and Dispersion Structure in Deep Drill Data. Statistical Modelling, 3, 43-64.
Kauermann, G. and Tutz, G. (2003) Semi- and nonparametric modelling of ordinal data. Journal of Computational and Graphical Statistics, 12, 176-196.
Kauermann, G. (2002) On a Small Sample Adjustment for the Profile Score Function in Semiparametric Smoothing Models. Journal of Multivariate Analysis, 82, 471-485.
Kauermann, G. and Carroll, R.J. (2001) A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association, 96, 1387-1396.
Kauermann, G. and Tutz, G. (2001) Testing generalized linear and semiparametric models against smooth alternatives. Journal of the Royal Statistical Society, Series B, 63, 147-166.
Galindo, C., Kauermann, G., Liang, H. and Carroll, R. (2001) Bootstrap confidence intervals for local likelihood, local estimating equations and varying coefficient models. Statistica Sinica, 11, 121-134.
Friedl, H. and Kauermann, G. (2000) Standard errors for EM estimates in variance component models. Biometrics, 56, 761-767.
Kauermann, G. (2000) Modeling longitudinal data with ordinal response by varying coefficients. Biometrics, 56, 692-698.
Kauermann, G. and Tutz, G. (2000) Local likelihood estimation in varying-coefficient models including additive bias correction. Journal of Nonparametric Statistics, 12, 343-371.
Kauermann, G. and Tutz, G. (1999) On model diagnostics using varying coefficient models. Biometrika, 86, 119-128.
Kauermann, G., Müller, M. and Carroll, R.J. (1998) The efficiency of bias-corrected estimators for nonparametric kernel estimation based on local estimation equations. Statistics & Probability Letters, 37, 41-47.
Kauermann, G. (1997) A note on multivariate logistic models for contingency tables.
Australian Journal of Statistics, 39, 261-276.
Tutz, G. and Kauermann, G. (1997) Local estimators in multivariate generalized linear models with varying coefficients. Computational Statistics, 12, 193-208.
Kauermann, G. (1996) On a dualization of graphical Gaussian models. Scandinavian Journal of Statistics, 23, 105-116.