Table 1.
Model | intercept | Lasso | Li&Li | lik.boost | PathBoost |
1 | 762.5 (14.4) | 83.6 (2.6) | 42.5 (1.1) | 83.4 (2.4) | 61.0 (1.7) |
2 | 305.8 (5.1) | 91.0 (2.7) | 80.8 (1.9) | 89.7 (2.7) | 64.8 (1.8) |
3 | 215.6 (4.1) | 32.6 (0.9) | 24.9 (0.8) | 32.1 (0.9) | 26.5 (0.7) |
4 | 131.1 (2.4) | 32.6 (0.9) | 29.9 (0.7) | 32.5 (0.9) | 26.9 (0.7) |
5 | 525.7 (9.9) | 87.9 (2.6) | 61.6 (1.5) | 85.6 (2.2) | 62.2 (1.6) |
6 | 171.6 (3.3) | 32.9 (0.9) | 27.6 (0.7) | 32.2 (0.9) | 26.9 (0.8) |
Predictive mean squared error, mean and standard errors (in parentheses), for an intercept-only model, the Lasso, the pathway-based procedure proposed in [9] (Li&Li), componentwise likelihood-based boosting (lik.boost), and boosting with pathway information (PathBoost) for six types of generating models.