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• W2952728306 abstract "A method of `network filtering' has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of detection seems a priori difficult to achieve, especially since the number of observations available often is much smaller than the number of variables describing the effects of the underlying network. Under the assumption that the network possesses a certain sparsity property, we provide a formal characterization of the accuracy with which the external effects can be detected, using a network filtering system that combines Lasso regression in a sparse simultaneous equation model with simple residual analysis. We explore the implications of the technical conditions underlying our characterization, in the context of various network topologies, and we illustrate our method using simulated data." @default.
• W2952728306 created "2019-06-27" @default.
• W2952728306 creator A5022485637 @default.
• W2952728306 creator A5075246947 @default.
• W2952728306 date "2009-02-21" @default.
• W2952728306 modified "2023-09-24" @default.
• W2952728306 title "Target Detection via Network Filtering" @default.
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• W2952728306 hasPublicationYear "2009" @default.
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