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Yuan, M. and Lin, Y. (2007) Model Selection and Estimation in the Gaussian Graphical Model. Biometrika, 94, 19-35.
https://doi.org/10.1093/biomet/asm018

has been cited by the following article:

• TITLE: Positive-Definite Sparse Precision Matrix Estimation

  AUTHORS: Lin Xia, Xudong Huang, Guanpeng Wang, Tao Wu

  KEYWORDS: Positive-Definiteness, Sparsity, D-Trace Loss, Accelerated Gradient Method

  JOURNAL NAME: Advances in Pure Mathematics, Vol.7 No.1, January 23, 2017

  ABSTRACT: The positive-definiteness and sparsity are the most important property of high-dimensional precision matrices. To better achieve those property, this paper uses a sparse lasso penalized D-trace loss under the positive-definiteness constraint to estimate high-dimensional precision matrices. This paper derives an efficient accelerated gradient method to solve the challenging optimization problem and establish its converges rate as . The numerical simulations illustrated our method have competitive advantage than other methods.