Table 3:
(High-dimensional settings: n = 100, p = 2000) Comparison of BAR, Lasso, SCAD, MCP, Adaptive Lasso (ALasso), and TLP Following the Sparsity-Restricted Least Squares Estimate (sLSE). (Misclassification = Mean Number of Misclassified Non-zeros and Zeros; FP = Mean Number of False Positives (Non-zeros); FN = Mean Number of False Negatives (Zeros); TM = Probability that the Selected Model is Exactly the True Model; MAB = Mean Absolute Bias; MSPE = Mean Squared Prediction Error from Five-Fold CV.)
| Model | Method | Misclassification | FP | FN | TM | MAB | MSPE |
|---|---|---|---|---|---|---|---|
| 1 | sLSE-BAR | 0.32 | 0.18 | 0.14 | 75.72% | 0.55 | 1.04 |
| sLSE-Lasso | 5.10 | 4.96 | 0.14 | 3.8% | 0.97 | 1.13 | |
| sLSE-SCAD | 0.94 | 0.80 | 0.14 | 64.60% | 0.57 | 1.05 | |
| sLSE-MCP | 0.67 | 0.53 | 0.14 | 68.32% | 0.57 | 1.05 | |
| sLSE-ALasso | 0.92 | 0.78 | 0.14 | 63.88% | 0.61 | 1.05 | |
| sLSE-TLP(0.15) | 0.74 | 0.60 | 0.14 | 56.56% | 0.64 | 1.06 | |
| sLSE-TLP(0.5) | 0.89 | 0.75 | 0.14 | 67.72% | 0.57 | 1.06 | |
| 2 | sLSE-BAR | 0.30 | 0.18 | 0.12 | 0% | 1.45 | 1.04 |
| sLSE-Lasso | 5.09 | 4.98 | 0.11 | 0% | 1.87 | 1.13 | |
| sLSE-SCAD | 0.95 | 0.83 | 0.12 | 0% | 1.47 | 1.05 | |
| sLSE-MCP | 0.66 | 0.54 | 0.12 | 0% | 1.47 | 1.05 | |
| sLSE-ALasso | 0.93 | 0.81 | 0.12 | 0% | 1.51 | 1.05 | |
| sLSE-TLP(0.15) | 0.72 | 0.60 | 0.12 | 0% | 1.53 | 1.06 | |
| sLSE-TLP(0.5) | 0.91 | 0.79 | 0.12 | 0% | 1.47 | 1.07 |