2007 ModelElectionAndEstimation

• (Yuan & Lin, 2007) ⇒ Ming Yuan, and Yi Lin. (2007). “Model Election and Estimation in the Gaussian Graphical Model.” In: Biometrica, 90. doi:10.1093/biomet/asm018.

Subject Headings: Covariance Selection Task Covariance Selection, Lasso, Maxdet Algorithm, Nonnegative Garrote, Penalized Likelihood.

Cited By

• ~ 176 http://scholar.google.com/scholar?q=%22Model+Selection+and+Estimation+in+the+Gaussian+Graphical+Model%22+2007

Quotes

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.

References

• H. Li, and J. Gui. (2006). “Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks.” In: Biostatistics 7.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 ModelElectionAndEstimation	Ming Yuan Yi Lin			Model Election and Estimation in the Gaussian Graphical Model			http://www2.isye.gatech.edu/~myuan/papers/graph.final.pdf	10.1093/biomet/asm018