Efficient Regularized Regression with Penalty for Variable Selection and Network Construction

Table 1

Performance measures for and regularized regression over 100 simulations, where values in the parentheses are the standard deviations. SF: number of average selected features; MSE: average mean squared error; : average absolute bias when comparing true and estimated parameters.

SF	MSE		# SF	MSE
0	3.39 (±1.1)	1.01 (±0.14)	0.206 (±0.12)	14.5 (±3.45)	1.19 (±0.19)	0.38 (±0.1)
0.3	3.37 (±0.9)	1.02 (±0.16)	0.23 (±0.12)	14.5 (±2.91)	1.21 (±0.19)	0.41 (±0.19)
0.6	3.49 (±1.7)	1.02 (±0.23)	0.23 (±0.16)	13.5 (±3.0)	1.26 (±0.2)	0.54 (±0.15)
0.8	3.32 (±0.9)	1.06 (±0.15)	0.28 (±0.21)	11.7 (±2.69)	1.3 (±0.21)	0.89 (±0.25)