. Author manuscript; available in PMC: 2016 Jun 1.

Published in final edited form as: J R Stat Soc Series B Stat Methodol. 2015 Jul 6;78(2):487–504. doi: 10.1111/rssb.12123

Table 1.

Comparison of inverse covariance matrix estimation errors in there data generating models. The parameter estimation error with regard to the matrix ℓ₁, ℓ₂, and Frobenius norms (denoted as ℓ_F here) is provided with standard deviations provided in the brackets. The results are obtained by 1,000 simulations.

		KSE			naive

Setting 1	n_grow = n_decay	ℓ₁	ℓ₂	ℓ_F	ℓ₁	ℓ₂	ℓ_F
	20	3.25(0.232)	1.53(0.104)	4.42(0.220)	5.02(0.287)	2.68(0.132)	8.30(0.412)
	100	2.72(0.165)	1.30(0.088)	3.78(0.204)	4.85(0.467)	2.55(0.117)	8.13(0.453)

Setting 2	n_grow
	40	3.39(0.553)	1.56(0.213)	4.47(0.302)	5.26(0.740)	2.73(0.313)	8.24(0.386)
	200	3.40(0.507)	1.57(0.147)	4.33(0.284)	5.19(0.740)	2.71(0.280)	8.34(0.352)

Setting 3	n_ed
Setting 3	50	2.21(0.194)	1.37(0.120)	3.20(0.104)	1.60(0.249)	0.84(0.113)	3.09(0.185)

		GGL			Guo

Setting 1	n_grow=n_decay	ℓ₁	ℓ₂	ℓ_F	ℓ₁	ℓ₂	ℓ_F
	20	3.28(0.298)	1.45(0.112)	4.13(0.190)	3.22(0.418)	1.42(0.259)	4.04(0.280)
	100	3.27(0.324)	1.42(0.100)	4.18(0.222)	3.38(0.474)	1.41(0.169)	4.31(0.335)

Setting 2	n_grow
	40	3.47(0.580)	1.47(0.163)	4.22(0.153)	3.06(0.417)	1.40(0.274)	4.00(0.205)
	200	3.22(0.618)	1.44(0.198)	4.08(0.199)	3.71(0.493)	1.73(0.264)	4.46(0.361)

Setting 3	n_ed
Setting 3	50	1.52(0.224)	0.85(0.105)	2.04(0.104)	1.48(0.263)	0.67(0.116)	1.81(0.150)