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Members of the IFAS with PhD. supervise Bachelor and Master theses in Statistics, preferably in the following areas and serve as examiners for the Master’s examination:
Nonparametric Statistics | Modelling of High-Dimensional Data, Approximative Inference, Biostatistics and Modelling of Genomic Data |
Nonparametric Statistics | Computational Statistics, Bayes Statistics, Experimental Design |
Nonparametric Statistics | Experimental Design, Econometrics, Spatial Statistics |
| Muszynska-Spielauer | |
Nonparametric Statistics | Population Health and Mortality |
Nonparametric Statistics | Time-Series Analysis, Analysis of Longitudinal Data, Survival Analysis, Bayes-Statistics, Generalized Linear Models |
Nonparametric Statistics | (Generalized) Linear Models, Factor Analysis |
Nonparametric Statistics | |
Modelling of High-Dimensional Data, Approximative Inference, Biostatistics and Modelling of Genomic Data | |
Computational Statistics, Bayes Statistics, Experimental Design | |
Experimental Design, Econometrics, Spatial Statistics | |
| Muszynska-Spielauer | Population Health and Mortality |
Time-Series Analysis, Analysis of Longitudinal Data, Survival Analysis, Bayes-Statistics, Generalized Linear Models | |
(Generalized) Linear Models, Factor Analysis |
Currently, amongst others, the following topics for Master theses are offered for supervision
• Statistical Modelling of Genomic Data | • Simplicial distance criteria for optimal discrimination design when the observations are correlated • Using Bayesian optimization for noisy objectives in Bayesian experimental design
|
• Statistical Modelling of Genomic Data | • Further aspects of the virtual noise method for optimal design of experiments. |
• Statistical Modelling of Genomic Data | • Gender differences in the inequality of healthy lifespans, |
• Statistical Modelling of Genomic Data | • Indirect Questioning Designs for Sensitive Variables |
| Waldl: | |
• Statistical Modelling of Genomic Data | • Empirical kriging variance in spatio-temporal models |
• Statistical Modelling of Genomic Data | |
• Simplicial distance criteria for optimal discrimination design when the observations are correlated • Using Bayesian optimization for noisy objectives in Bayesian experimental design
| |
• Further aspects of the virtual noise method for optimal design of experiments. | |
• Gender differences in the inequality of healthy lifespans, | |
• Indirect Questioning Designs for Sensitive Variables | |
| Waldl: | • Empirical kriging variance in spatio-temporal models |
