Flexible sparse dynamic network models
Prof Ernst Wit (University of Groningen)
Friday 17th October, 2014 15:00-16:00 Maths 203
A graph is one possible way to describe complex relationships between many actors, such as for example RNA, proteins and metabolites. In many cases, genomic data comes from large monotoring systems with no prior screening. The combination of such indiscrimate data collection combined with the structured nature of genomic interactions, the actual set of relationships, therefore, tends to be sparse.
When data is obtained from noisy measurements of (some of) the nodes in the graph, then graphical models present an appealing and insightful way to describe graph-based dependencies between the random variables. Although potentially still interesting, the main aim of inference is not the precise estimation of the parameters in the graphical model, but the underlying structure of the graph.
Graphical lasso and related methods opened up the field of sparse graphical model inference in high-dimensions. We extend this type of models to a set of dynamic network models, always with a penalty in order to induce sparsity. Selecting the tuning parameter in such settings can be a harrowing task. We show how extensions of leave-one-out cross-validation and the generalized information criterion are able to deal with determining the underlying graph in an efficient way.