Program

Concept

Our main goal is to provide doctoral candidates with a perspective on the methodical foundations of Statistics that goes far beyond a specialization in the life, social or economic sciences or the humanities and to . institutionalize a scientific dialogue through interdisciplinary applications. This is to ensure that, going forward, we can rely on a common academic foundation and language as well as a diversity of methods for the description and modeling of uncertainty in various applications.

The function of statistics as a transdisciplinary bridge, and specifically the transfer of methods between projects in the natural (MC-Health, CHI) and social sciences (CEQURA, CEST), is provided with an institutional basis through this program.

A Master’s degree in Statistics,Informatics, Mathematics or a related discipline is required for the admission to this doctoral program. Exceptions can be made depending on individual qualifications.

Profile

Structure

Participants of the program will profit directly from the international reputation of the professors and junior scientists doing research at the Institute of Statistics. They will be given the unique opportunity to simultaneously contribute to scientific progress in statistical methodology as well as the scientific progress of other sciences through their applied projects. In this manner, they will acquire key skills for any academic or non-academic career.

Content

The contents of the program are derived from the fields of research in explorative, inductive and computational statistics that are represented at the Department of Statistics. The focus lies on the following topics:

• Analysis of Networkdata
• Description of unobserved heterogeneity and general uncertainty
• Computational aspects of statistical inference
• Development and evaluation of frequentist and bayesian regression models
• Supervised learning, classification and nonlinear regression
• Machine Learning
• Biosignal Processing
• Development of methods for inaccurate/corrupted and missing data
• Development, analysis and implementation of statistical methods of analysis
• Calibration
• Multilevel Modeling
• Functional Data Analysis
• Methods for measuring latent constructs
• Model Averaging
• Model Selection and Diagnosis
• Multivariate procedures
• Planing and evaluation of scientific experiments
• Forecasting
• Spatial and spatio-temporal structures and imaging
• Visualization techniques
• Epistemological foundations

Courses

Colloquium of the Department

The regular colloquium presents and discusses current topics in Statistics through lectures by professors or guests.

Summer Retreat

The summer retreat is a two-day intramural conference where the participants present the progress of their work in front of the group.

In particular the PhD students should get familiar with the process of organizing a scientific conference. There is also the option of participating in the program of the GraduateCenter.

Graduation

The terms of the actual procedure of promotion are applicable (see the information page of the facutly 16 only available German).