Model selection and estimation in the Gaussian graphical model

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ID: 291519
2007
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Abstract
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead to a sparse and shrinkage estimator of the concentration matrix that is positive definite, and thus conduct model selection and estimation simultaneously. The implementation of the methods is nontrivial because of the positive definite constraint on the concentration matrix, but we show that the computation can be done effectively by taking advantage of the efficient maxdet algorithm developed in convex optimization. We propose a BIC-type criterion for the selection of the tuning parameter in the penalized likelihood methods. The connection between our methods and existing methods is illustrated. Simulations and real examples demonstrate the competitive performance of the new methods.
Reference Key
openalex_W2081746825 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ming Yuan, Yi Lin
Journal jurnal biometrika dan kependudukan
Year 2007
DOI
10.1093/biomet/asm018
URL
https://doi.org/10.1093/biomet/asm018
Keywords Keywords not found

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