Topology Adaptive Graph Estimation in High Dimensions

Johannes Lederer; Christian Müller

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arXiv · 1410.7279

Topology Adaptive Graph Estimation in High Dimensions

Johannes Lederer·Christian Müller

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Abstract

We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compare GTREX with standard methods on a new simulation set-up that is designed to assess accurately the strengths and shortcomings of different methods. These simulations show that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperforms other standard methods over a large spectrum of scenarios. Moreover, we show that GTREX can rival this scheme and, therefore, can provide competitive graph estimation without the need for tuning parameter calibration.

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Johannes Lederer, Christian Müller. 2014-10-27. Topology Adaptive Graph Estimation in High Dimensions. https://arxiv.org/abs/1410.7279

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