Events which have already happened.
Shape Analysis: Infinite-Dimensional Geometry, Statistics on Manifolds, and Applications
Starke Gauß'sche Approximation des Rasch-Mischungsmodells mit Anwendungen
Reconstructing branching lineages in single cell genomics
A short trip through the tree of life: from Ebola over Diphtheria and Tuberculosis to Penguins
New Concepts for Reliable Assessment of Statistical Methods
Capital allocation for dynamic risk measures
Competing selective sweeps
Statistical phenomena in hospital epidemiology: Challenges for statisticians and clinicians
Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of this type of risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. We propose a novel approach to measuring systemic risk. Key to our construction is a rigorous derivation of systemic risk measures from the structure of the underlying system and the objectives of a financial regulator. The suggested systemic risk measures express systemic risk in terms of capital endowments of the financial firms. Their definition requires two ingredients: first, a random field that assigns to the capital allocations of the entities in the system a relevant stochastic outcome. The second ingredient is an acceptability criterion, i.e. a set of random variables that identifies those outcomes that are acceptable from the point of view of a regulatory authority. Systemic risk is measured by the set of allocations of additional capital that lead to acceptable outcomes. The resulting systemic risk measures are set-valued and can be studied using methods from set-valued convex analysis. At the same time, they can easily be applied to the regulation of financial institutions in practice. We explain the conceptual framework and the definition of systemic risk measures, provide an algorithm for their computation, and illustrate their application in numerical case studies. We apply our methodology to systemic risk aggregation as described in Chen, Iyengar & Moallemi (2013) and to network models as suggested in the seminal paper of Eisenberg & Noe (2001), see also Cifuentes, Shin & Ferrucci (2005), Rogers & Veraart (2013), and Awiszus & Weber (2015). This is joint work with Zachary G. Feinstein and Birgit Rudloff.
Parameter selection for nonlinear modeling using L1 regularization
Statistical analysis of modern sequencing data – quality control, modelling and interpretation
Risk sensitive utility indifference pricing of perpetual American options under fixed transaction costs
Value-at-Risk aggregation under uncertainty
Developing Prediction Models and Visualization Tools for Predictive Toxicology
How superadditive can a risk measure be?
Geometrisch-optische Illusionen und Riemannsche Geometrie
Causal Discovery From Bivariate Relationships
Quantum Physics and Big Data
Uncertainty analyses in the systems biology using the profile likelihood