A new method for model selection for Gaussian directed acyclic graphs (DAG) and Gaussian graphical models (GGM), with extensions towards ancestral graphs (AG), is constructed to have good prediction properties. The method is based on the focused information criterion, and offers the possibility...

Connectivity in the brain is the most promising approach to explain human behavior. Here we develop a focused information criterion for graphical models to determine brain connectivity tailored to specific research questions. All efforts are concentrated on high-dimensional settings where the...

A focused information criterion is developed to estimate undirected graphical models where for each node in the graph a generalized linear model is put forward conditioned upon the other nodes in the graph. The proposed method selects a graph with a small estimated mean squared error for a...

We present a focused selection method for social networks. The procedure is driven by a focus, the main quantity we want to estimate well. It represents the statistical translation of a research hypothesis into parameters of interest. Given a collection of models, the procedure estimates for...

A new methodology for selecting a Bayesian network for continuous data outside the widely used class of multivariate normal distributions is developed. The ‘copula DAGs' combine directed acyclic graphs and their associated probability models with copula C/D-vines. Bivariate copula densities...