- Previous workplaces:
- Faculty of Statistics, TU Dortmund University
- Department of Mathematics, Ruhr-Universität Bochum
(very short bio)
2005 Diplom (Mathematics), Dresden University of Technology
2010 Dr. rer. nat. (Statistics), TU Dortmund University
2015 Lecturer, University of Aberdeen
My research area is data analysis: development of methods and their application.
I am interested in the whole sprectrum of data-analytical tasks and all areas of application - from classical statistical-inference methods to modern computational methods that go under the name 'data science'.
On the methodology side, I do care for the mathematical foundations. They bring certainty into a field which is like no other characterized by uncertainty. Understanding them is the basis for developing new and better tools. Some topics I have worked on can be under Research.
If you have data-analytical questions of whatever kind, feel free to get in touch.
Below you find several topics I have worked on in the recent past.
(Full texts of most of the papers can be downloaded under Publications.)
We develop tools for detecting changes in certain characteristics of an observed time series, such as the mean (Vogel & Wendler 2017) or the variance (Gerstenberger, Vogel, Wendler 2017, currently reviewed) or the cross-sectional dependence (Dehling, Vogel, Wendler, Wied 2016, Vogel & Fried 2015). Classical test for this purpose are based on estimates for the second moments. These are not very well suited for heavy-tailed data, which, in many areas of applications, are rather the norm than the exception. The common theme of our work is an improved efficiency under heavy tails (actually moment-free) - while retaining the same performance under normality.
Graphical models provide a powerful tool to model complex systems with uncertainties. Conditional dependencies are represented by the edges of a graph, and graph-theoretic methods are employed in their analysis. The traditional working assumption is multivariate normality, which leads to the term “Gaussian graphical models” and allows a statistical inference based on the maximum-likelihood paradigm. We extend this to the semi-parametric class of elliptical distributions and show that graphical modelling can be based upon any covariance matrix estimator - as long as it satisfies two natural conditions: asymptotic normality and affine equivariance (Vogel & Fried 2011). We also propose Graphical M-estimators (Vogel & Tyler 2014) for robustly fitting graphical models. These work also for n < p.
Music performance anxiety (MPA)
We investigate the origins of music performance anxiety (i.e., chronic stage fright): how it is related to childhood experiences and other types of anxieties. This is fundamental research aimed at finding effective therapies for MPA. And it is a nice application of a variaty of methods of multivariate statistics - graphical models being one of them. My co-author Anna Wiedemann recently gave a talk in Reykjavik (Wiedemann et al. 2017).
Robust high-dimensional correlation estimation: spherical correlation
The sample Pearson correlation matrix has a variety of very good statistical properties, among them: (1) it is guaranteed to be non-negative definite, (2) it is very fast to compute, and (3) it can be computed if n < p, i.e., if the number of variables exceeds the number of observations.
However, it has one disadvantage: it does note cope well with heavy-tailed data. Alternative correlation matrix estimators that overcome that drawback usually fail at least one of the above three. Dürre, Fried, Vogel 2017 describe a correlation matrix estimator that is very robust wrt heay tails (and defined without any moment assumption) and possesses the three desirable properties above. This work is based on a series of earlier papers that lay the foundations: Dürre, Vogel, Tyler 2014; Dürre, Vogel, Fried 2015; Dürre, Vogel 2016; Dürre, Tyler, Vogel 2016.
Gini's mean difference derives its name from its appearance in a 1912 paper by Corrado Gini. It is the enumerator of the Gini ratio and as such often used, but as scale measure it has led much of a wallflower life in statistics. Maybe unfairly so: we show that it has very good statistical properties (Gerstenberger & Vogel 2014)
The distance standard deviation is another scale measure, which is related to the much acclaimed distance correlation. (Edelmann, Richards, Vogel 2016 currently reviewed)
- Further Info
Go Abroad Tutor Physics
- 22 November, 3p.m., (place TBA): Tobias Kley from the LSE gives a talk on "Sequential detection of structural changesin irregularly observed panel data"
- 25 November (Saturday): I will venture out to the south part of town, to the banks of the Royal Dee and do a Maths Masterclass.
- 12/2017: I give a talk at the CMStatistics 2017 in Roland Fried's session "Outliers and change-points in time series".
- 25th October 2017: I give a talk at Lancaster University.
- 21 October 2017: Open Day at the University of Aberdeen
STUDY PHYSICS AT ABERDEEN!
(Magdalena is still working on the thumbs-up.)
- August 2017: Martin Wendler from the University of Greifswald gives a short course on nonparametric statistics.
- KU Leuven, Belgium, 4 May 2017, Seminar talk on change-point detection for scale changes.
- CMStatististics, Seville, Spain, 9-11 December 2016. I organize the session EO147: Multivariate analysis and change-point detection.
- German Statistical Week, Augsburg, Germany, 13-16 September 2016. I am invited speaker in the Section "Nonparametric and Robust Statistics".
- University of Greifswald, Germany, July/August 2016. I work together with Martin Wendler on change-point analysis using U-statistics and U-quantiles and gave a talk on robust correlation estimation.
- Cosmos Summer School, Aberdeen, UK, 27 June - 6 July 2016. I give two lectures on the various ways of comparing two data samples.
- ISNPS Conference, Avignon, France, 11-16 June 2016.
- Queen Mary University of London, UK, May 2016. Together with Carina Gerstenberger, I worked on change-point tests for scale changes. I gave a talk on the use of U-quantiles for change-point analysis.
Some time ago
This video features my introductory statistics course at the University of Bochum.