Estimation of graphical models using the L1,2 norm
提出结构化GLASSO（SGLASSO）估计量，用L1,2混合范数替代L1,1范数，在控制稀疏性的同时保留结构信息，模拟和实证均优于传统GLASSO，适用于经济网络分析。
Gaussian graphical models are recently used in economics to obtain networks of dependence among agents. A widely used estimator is the graphical least absolute shrinkage and selection operator (GLASSO), which amounts to a maximum likelihood estimation regularized using the L1,1 matrix norm on the precision matrix Ω. The L1,1 norm is a LASSO penalty that controls for sparsity, or the number of zeros in Ω. We propose a new estimator called structured GLASSO (SGLASSO) that uses the L1,2 mixed norm. The use of the L1,2 penalty controls for the structure of the sparsity in Ω. We show that when the network size is fixed, SGLASSO is asymptotically equivalent to an infeasible GLASSO problem which prioritizes the sparsity‐recovery of high‐degree nodes. Monte Carlo simulation shows that SGLASSO outperforms GLASSO in terms of estimating the overall precision matrix and in terms of estimating the structure of the graphical model. In an empirical illustration using a classic firms' investment data set, we obtain a network of firms' dependence that exhibits the core–periphery structure, with General Motors, General Electric and US Steel forming the core group of firms.