Bachelor's Thesis

Bachelor's Thesis are written in the course Seminar aus Statistik.

The formal supervision is held by the lectorer of the seminar.
The content-related supervisian can be performed by any member of IFAS with a doctoral degree.

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:

Duller	Nonparametric Statistics
Futschik	Modelling of High-Dimensional Data, Approximative Inference, Biostatistics and Modelling of Genomic Data
Grün	Computational Statistics, Cluster Analysis, Mixture Distributions
Müller	Experimental Design, Econometrics, Spatial Statistics
Quatember	Survey Statistics
Stehlík	Risk Management, Experimental Design, Pension Systems
Wagner	Time-Series Analysis, Analysis of Longitudinal Data, Survival Analysis, Bayes-Statistics, Generalized Linear Models
Waldl	(Generalized) Linear Models, Factor Analysis

The collection of completed Bachelor's thesis can be found here:

Information regarding the Bachelor's Thesis can be found here. The template for the layout of the Bachelor's Thesis will be provided in the Seminar aus Statistik (via KUSSS).

Two Bachelor's Thesis graded as "very good" can be found here (in German)

Magdalena Leitner: Portfolio-VaR-Schätzung mittels bedingter Compula-GARCH Methode
(Portfolio-VaR-Estimation using a conditional Compula-GARCH Method

Value-at-Risk (VaR) is an important instrument for risk assessment of financial time series. This estimation can be performed in different ways. One possibility is modelling with the aid of GARCH-models and copula functions, which is a very flexible approach. Within this bachelor's thesis the Copula-GARCH method is compared with classic VaR-estimation methods based on the stocks ATX and DAX.

Daniel Dober: Distanz- und modellbasiertes Clustern eines phänotypischen Datensatzes
(Distance- and modelbased clustern of a phenotypic data set)

This bachelor's thesis conducts a cluster analysis of a phenotypic data set in order to detect class structures with the aid of "unsupervised learning". Therefore, the results of a distance-based cluster analysis, which is generally used for these problems, are compared with those of a model-based cluster analysis. The resulting class structure was used to identify genes having significant effects on the maturity of barley plants.