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. Author manuscript; available in PMC: 2014 Feb 16.
Published in final edited form as: Stat Interface. 2013 Apr 1;6(2):243–259. doi: 10.4310/sii.2013.v6.n2.a8

Table 1.

Mean L1 losses (and standard deviations) for the different methods

Type CLASSO SPICE ACLASSO CSCAD BCLASSOm BCLASSOs
Sparse Θ 2.38(0.43) 4.18(1.91) 5.71(2.62) 14.27(12.18) 4.82 (1.11) 4.52 (0.87)
Σ 4.03(1.03) 2.99(0.76) 5.65(1.42) 19.02(7.92) 13.65(1.70) 3.56 (0.67)
Moderately Θ 3.29(0.52) 5.09(2.69) 6.07(2.74) 13.44(8.80) 6.09 (1.29) 5.79 (1.03)
Sparse Σ 5.76(1.42) 4.17(0.77) 6.41(1.25) 22.84(9.61) 12.93(1.89) 5.10 (0.87)
Dense Θ 4.90(0.53) 7.08(1.83) 7.65(2.46) 17.22(11.49) 6.87 (1.17) 6.39 (0.87)
Σ 9.53(2.24) 6.35(1.04) 9.95(1.31) 31.91(27.49) 12.82(1.80) 6.04 (0.69)
AR(1) Θ 5.44(0.57) 7.60(2.16) 8.12(2.14) 13.11(7.14) 7.02 (1.15) 7.00 (0.87)
Σ 9.53(2.24) 7.48(1.29) 7.95(1.31) 31.91(27.49) 12.42(5.97) 5.97 (0.59)
Tridiagonal Θ 5.70(0.57) 7.99(1.83) 7.80(2.32) 19.45(9.45) 10.43(1.42) 9.27 (0.95)
Σ 11.37(2.65) 9.37(1.79) 9.19(1.48) 24.50(9.10) 12.79(1.82) 12.18(1.16)
Diagonal Θ 2.41(2.75) 3.69(2.11) 7.00(3.79) 10.49(5.87) 4.31 (1.46) 3.98 (0.98)
Σ 4.23(4.74) 2.43(0.79) 7.27(5.44) 18.47(9.72) 13.81(1.67) 4.47 (0.99)

Note: CLASSO = covariance lasso; ACLASSO = adaptive covariance lasso; SPICE = sparse permutation invariant covariance estimator; ACLASSO = adaptive covariance lasso; CSCAD = smoothly clipped absolute deviation for covariance; BCLASSOm = Bayesian covariance; lasso with L1 minimax estimator; BCLASSOs = Bayesian covariance lasso with sparsity forced;