Sensitivity of graphical modeling against contamination

Graphical modeling as a form of multivariate analysis has turned out to be a capable tool for the detection and modeling of complex dependency structures. Statistical models are related to graphs, in which variables are represented by points and associations between each two of them as lines. The usefulness of graphical modeling depends of course on finding a graphical model, which fits the data appropriately. We will investigate how existing model building strategies and estimation methods can be affected by model disturbances or outlying observations. The focus of our sensitivity analysis lies on mixed graphical models, where both discrete and continuous variables are considered.