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arXiv · 1208.4275

Composite likelihood estimation of sparse Gaussian graphical models with symmetry

Xin Gao·Helene Massam
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Abstract

In this article, we discuss the composite likelihood estimation of sparse Gaussian graphical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the likelihood estimation can be computational intensive. The composite likelihood offers an alternative formulation of the objective function and yields consistent estimators. When a sparse model is considered, the penalized composite likelihood estimation can yield estimates satisfying both the symmetry and sparsity constraints and possess ORACLE property. Application of the proposed method is demonstrated through simulation studies and a network analysis of a biological data set.

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Xin Gao, Helene Massam. 2012-08-21. Composite likelihood estimation of sparse Gaussian graphical models with symmetry. https://arxiv.org/abs/1208.4275

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