A focused information criterion for graphical models

A new method for model selection for Gaussian Bayesian networks and Markov networks, with extensions towards ancestral graphs, is constructed to have good mean squared error properties. The method is based on the focused information criterion, and offers the possibility of fitting individual-tailored models. The focus of the research, that is, the purpose of the model, directs the selection. It is shown that using the focused information criterion leads to a graph with small mean squared error. The low mean squared error ensures accurate estimation using a graphical model; here estimation rather than explanation is the main objective. Two situations that commonly occur in practice are treated: a data-driven estimation of a graphical model and the improvement of an already pre-specified feasible model. The search algorithms are illustrated by means of data examples and are compared with existing methods in a simulation study.status: publishe

A focused information criterion for graphical models

A new method for model selection for Gaussian Bayesian networks and Markov networks, with extensions towards ancestral graphs, is constructed to have good mean squared error properties. The method is based on the focused information criterion, and offers the possibility of fitting individual-tailored models. The focus of the research, that is, the purpose of the model, directs the selection. It is shown that using the focused information criterion leads to a graph with small mean squared error. The low mean squared error ensures accurate estimation using a graphical model; here estimation rather than explanation is the main objective. Two situations that commonly occur in practice are treated: a data-driven estimation of a graphical model and the improvement of an already pre-specified feasible model. The search algorithms are illustrated by means of data examples and are compared with existing methods in a simulation study

A focused information criterion for graphical models in fMRI connectivity with high-dimensional data

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 number of nodes in the graph is larger than the number of samples. The graphical models may include autoregressive times series components, they can relate graphs from different subjects or pool data via random effects. The proposed method selects a graph with a small estimated mean squared error for a userspecified focus. The performance of the proposed method is assessed on simulated data sets and on a resting state functional magnetic resonance imaging (fMRI) data set where often the number of nodes in the estimated graph is equal to or larger than the number of samples

A focused information criterion for graphical models in fMRI connectivity with high-dimensional data

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 number of nodes in the graph is larger than the number of samples. The graphical models may include autoregressive times series components, they can relate graphs from different subjects, or pool data via random effects. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus. The performance of the proposed method is assessed on simulated datasets and on a resting state functional magnetic resonance imaging (fMRI) dataset where often the number of nodes in the estimated graph is equal to, or larger than the number of samples.status: publishe