Regularisierung für diskrete Datenstrukturen

• Antragsteller: Professor Dr. Gerhard Tutz
• Fachliche Zuordnung: Statistik und Ökonometrie
• Förderung: Förderung von 2011 bis 2016
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 208398175

• Erstellungsjahr: 2015

Zusammenfassung der Projektergebnisse

Keine Zusammenfassung vorhanden

Projektbezogene Publikationen (Auswahl)

• Variable selection and shrinkage of varying to fixed effects in finite mixtures of generalized linear models. In: Muggeo, V. M. R., Capursi, V., Boscaino, G., und Lovison, G. (Eds.), Proceedings of the 28th International Workshop on Statistical Modelling, Vol. 1. 2013, S. 319–324. ISBN 978-88-96251-47-8.
  Pößnecker, W., Tutz, G.
  
• Clustering in linear mixed models with a group fused lasso penalty. Biometrical Journal, Vol. 56. 2014, Issue 1, pp. 44–68.
  Heinzl, Felix, Tutz, Gerhard
  
• Regularization and model selection with categorical predictors and effect modifiers in generalized linear models. Statistical Modelling, Vol. 14. 2014, issue: 2, pp. 157-177.
  Oelker, M.-R., Gertheiss, J., Tutz, G.
  
• Regularization and selection of proportional versus nonpro-portional effects in sequential logit models. In: T. Kneib, F. Sobotka, J. Fahrenholz, und H. Irmer (Eds.), Proceedings of the 29th International Workshop on Statistical Modelling, Vol. 1, S. 279–284.
  Pößnecker, W., Tutz, G.
  
• A uniform framework for the combination of penalties in generalized structured models. Advances in Data Analysis and Classification, published online.
  Oelker, M.-R., Tutz, G.
  
• Modeling electoral choices in multiparty systems with high-dimensional data: a regularized selection of parameters using the Lasso approach. Journal of Choice Modelling, Vol. 16. 2015, pp. 23–42.
  Mauerer, I., Pößnecker, W., Thurner, P., und Tutz, G.
  
• Selection and fusion of categorical predictors with L0-type penalties. Statistical Modelling, Vol. 15.2015, Issue 5, pp. 389 -410.
  Oelker, M.-R., Pößnecker, W., Tutz, G.
  
• Variable selection in general multinomial logit models. Computational Statistics & Data Analysis, Vol. 82. 2015, pp. 207–222.
  Tutz, G., Pößnecker, W., und Uhlmann, L.
  
• Additive Mixed Models with Approximate Dirichlet Process Mixtures: the EM Approach. Statistics and Computing, Vol. 26, 2016, Issue 1, pp. 73–92.
  Heinzl, F., Tutz, G.
  
• Modeling clustered heterogeneity: fixed effects, random effects and mixtures. International Statistical Review, Early View (Online Version of Record published before inclusion in an issue).
  Tutz, G., Oelker, M.-R.