Dr DANIEL VOGEL

Dr DANIEL VOGEL

Lecturer

Overview
Dr DANIEL VOGEL
Dr DANIEL VOGEL

Contact Details

Telephone
work +44 (0)1224 272469
Email
Address
The University of Aberdeen office: Meston 328

postal address:
Institute of Complex Systems and Mathematical Biology
University of Aberdeen
Meston Walk
King's College
Aberdeen
AB24 3UE
United Kingdom
 

Biography

(very short bio)
2005 Diplom (Mathematics), Dresden University of Technology
2010 Dr. rer. nat. (Statistics), TU Dortmund University
2015 Lecturer, University of Aberdeen

Research interests

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.

Research

Current Research

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.)

 

Change-point analysis

River Elbe discharge data

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

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)

how MPA relates to other anxietiesWe 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

true correlation matrix (500x500)two correlation matrix estimates at t_{2.5} distribution

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.

 

 

Scale measures

 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)

Collaborations

Further Info

Admin Responsibilities

Go Abroad Tutor Physics

Coming up

Recently

STUDY PHYSICS AT ABERDEEN!

 

(Magdalena is still working on the thumbs-up.)

Some time ago

This video features my introductory statistics course at the University of Bochum.

Publications

Publications 

update | about Staff Pages