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This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of...
This paper describes a new methodology and associated theoretical analysis for rapid and accurate extraction of activation regions from functional MRI data. Most fMRI data analysis methods in use today adopt a hypothesis testing approach, in which the BOLD signals in individual voxels or clusters of voxels are compared to a threshold. In order to obtain statistically meaningful results, the testing...
Rapid and accurate extraction of level sets and isoconcentration surfaces from noisy medical images is a common problem arising in a variety of contexts, such as estimating regions in which uptake of a pharmaceutical has exceeded some critical value or identifying areas of brain activity in neuroimaging. In general, a level set is the set 5 on which a function f exceeds a critical value (e.g. S =...
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