Table 2.

Comparison of the performances on estimating the precision matrix Θ by the two-stage procedure, the iterative selection procedure of [10], a neighbor-based selection procedure [14] and the Gaussian graphical model using glasso [13], where Δ = Θ − Θ̂.

Method	AUC	SPE	SEN	MCC	‖Δ‖∞	‖|Δ|‖∞	‖Δ‖2	‖Δ‖F
Model 1: (p, q, n)=(100, 100, 250)
Two-stage	0.91	0.99	0.49	0.56	0.32	1.18	0.68	3.24
Iterative	0.91	0.99	0.48	0.56	0.33	1.17	0.67	3.18
glasso	0.81	0.97	0.24	0.21	0.69	1.89	1.12	5.19
Neighbor	0.86	0.99	0.38	0.48
Model 2: (p, q, n)=(50, 50, 250)
Two-stage	0.91	0.97	0.69	0.65	0.35	1.31	0.73	2.43
Iterative	0.92	0.98	0.69	0.66	0.37	1.30	0.72	2.36
glasso	0.74	0.87	0.37	0.18	0.75	2.12	1.20	4.57
Neighbor	0.88	0.95	0.60	0.48
Model 3: (p, q, n)= (25, 10, 250)
Two-stage	0.89	0.91	0.76	0.62	0.23	0.90	0.51	1.20
Iterative	0.89	0.91	0.76	0.62	0.24	0.90	0.52	1.21
glasso	0.57	0.43	0.73	0.12	0.65	1.99	1.12	2.77
Neighbor	0.85	0.84	0.68	0.44
Model 4: (p, q, n)=(1000, 200, 250)
Two-stage	0.93	1	0.32	0.51	0.46	1.77	0.91	13.42
Iterative	0.90	1	0.31	0.47	0.59	1.81	0.97	13.48
glasso	0.88	0.98	0.08	0.02	0.71	2.86	1.31	19.82
Neighbor	0.87	1	0.12	0.16
Model 5: (p, q, n)=(800, 200, 250)
Two-stage	0.93	1	0.21	0.45	0.48	1.80	0.97	12.58
Iterative	0.89	1	0.21	0.34	0.75	2.30	1.20	12.82
glasso	0.87	0.97	0.07	0.02	0.76	2.97	1.40	18.39
Neighbor	0.87	0.96	0.61	0.19
Model 6: (p, q, n)=(400, 200, 250)
Two-stage	0.79	1	0.05	0.20	0.39	1.56	0.79	7.13
Iterative	0.75	1	0.05	0.21	0.44	1.55	0.77	6.86
glasso	0.71	0.95	0.03	−0.01	0.69	2.72	1.22	11.01
Neighbor	0.73	0.99	0.08	0.10