Table 2.

Comparison of the performance for simulated data sets of different dimensions when l₁ penalty functions are used.

Precision Matrix	Measure	MNGM	GGM-I	GGM-C GGM-R
	Model 3, n = 100, p = 150, q = 150
A	||ΔA||	0.12(0.014)	0.31(0.013)	0.78(0.094)
	|||ΔA|||∞	0.32(0.028)	0.59(0.027)	1.45(0.092)
	||ΔA||∞	0.05(0.011)	0.20(0.010)	0.49(0.074)
	||ΔA||F	0.61(0.069)	2.26(0.120)	4.72(0.802)
	SPEA	0.84(0.005)	0.80(0.004)	1.00(0.000)
	SENA	1.00(0.000)	1.00(0.000)	0.00(0.000)
	MCCA	0.45(0.006)	0.40(0.004)	0.05(0.022)
B	||ΔB||	0.10(0.009)	0.10(0.009)	0.77(0.186)
	|||ΔB|||	0.29(0.022)	0.32(0.025)	1.38(0.208)
	||ΔB||∞	0.04(0.007)	0.04(0.007)	0.58(0.236)
	||ΔB||F	0.53(0.025)	0.56(0.024)	4.25(0.856)
	SPEB	0.83(0.005)	0.80(0.003)	1.00(0.000)
	SENB	1.00(0.000)	1.00(0.000)	0.01(0.000)
	MCCB	0.43(0.007)	0.40(0.004)	0.07(0.021)
	Model 4, n = 100, p = 500, q = 500
A	||ΔA||	0.10(0.008)	0.22(0.008)	3.69(0.521)
	|||ΔA|||∞	0.27(0.018)	0.45(0.019)	4.23(0.502)
	||ΔA||∞	0.04(0.007)	0.14(0.006)	3.63(0.581)
	||ΔA||F	0.95(0.078)	2.94(0.131)	43.68(6.153)
	SPEA	0.99(0.001)	0.95(0.001)	1.00(0.002)
	SENA	1.00(0.00)	1.00(0.00)	0.01(0.038)
	MCCA	0.76(0.008)	0.52(0.003)	0.13(0.030)
B	||ΔB||	0.08(0.006)	0.08(0.006)	1.17(0.026)
	|||ΔB|||∞	0.26(0.019)	0.26(0.019)	6.88(0.809)
	||ΔB||∞	0.03(0.003)	0.03(0.004)	0.34(0.088)
	||ΔB||F	0.79(0.028)	0.76(0.031)	13.07(0.773)
	SPEB	0.98(0.001)	0.97(0.001)	0.64(0.055)
	SENB	1.00(0.000)	1.00(0.000)	0.65(0.095)
	MCCB	0.75(0.007)	0.62(0.003)	0.06(0.015)

MNGM: the matrix normal graphical model with l₁ penalties; GGM-I: Gaussian graphical model treating rows or columns as independent; GGM-R/GGM-C: Gaussian graphical model that uses only data from the first column or the first row. For each measurement, mean and standard deviation are calculated over 50 replications.