Research Article

Efficient Regularized Regression with Penalty for Variable Selection and Network Construction

Table 4

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

Measures

AIC SF3.26 (±0.54)3.72 (±1.94)4.8 (±2.77)
0.19 (±0.09)0.36 (±0.58)1.02 (±1.2)
MSE0.96 (±0.14)1.02 (±0.31)1.27 (±0.51)
true model78/10073/10059/100

BIC SF3.0 (±0.0)3.0 (±0.38)2.89 (±0.80)
0.16 (±0.08)0.45 (±0.69)1.80 (±1.20)
MSE0.97 (±0.15)1.29 (±0.81)2.48 (±1.17)
true model100/10094/10053/100