Dr. Felix Heinzl

Forschungsschwerpunkte

Heinzl, F. & G. Tutz (2016): Additive mixed models with approximate Dirichlet process mixtures: the EM approach. Statistics and Computing, 26(1), 73-92.

Vorausgehende Version: Technical Report No. 138, Department of Statistics, LMU München.

Giessing, S., F. Heinzl, B. Kleber & A. Wilke (2014): Geheimhaltung beim Zensus 2011. Wirtschaft und Statistik, November 2014, 641-647.

Heinzl, F. & G. Tutz (2014): Clustering in linear mixed models with a group fused lasso penalty. Biometrical Journal, 56(1), 44-68.

Vorausgehende Version: Technical Report No. 123, Department of Statistics, LMU München.

Heinzl, F. & G. Tutz (2013): Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm. Statistical Modelling, 13(1), 41-67.

Vorausgehende Version: Technical Report No. 115, Department of Statistics, LMU München.

Heinzl, F., L. Fahrmeir & T. Kneib (2012): Additive mixed models with Dirichlet process mixture and P-spline priors. Advances in Statistical Analysis, 96(1), 47-68.

Vorausgehende Version: Technical Report No. 68, Department of Statistics, LMU München.

Clustering in linear mixed models with Dirichlet process mixtures using EM algorithm, 16. Juli 2012,
IWSM 27 Prag
Linear mixed models with a penalized normal mixture as random effects distribution, 12. September 2011,
CEN 2011 Zürich
EM algorithm for linear mixed models with Dirichlet process mixtures, 13. September 2010,
16. DStatG-Nachwuchsworkshop Nürnberg
Additive mixed models with Dirichlet process mixture and P-Spline priors, 25. März 2010,
DAGStat2010 Dortmund

R-Paket clustmixed zum Clustern in linearen und additiven gemischten Modellen
Mitarbeit am R-Paket BayesX
Additiv gemischte Modelle mit Dirichlet-Prozess-Mischungs- und P-Spline-Prioris: amm.dpm.mcmc