Research Article
Variable Selection and Parameter Estimation with the Atan Regularization Method
Table 1
Simulation results for linear regression models of Example
8.
| Method | MRME | Number of zeros | Proportion of | C | IC | Underfit | Correct-fit | Overfit |
| , | Lasso | 0.6213 | 3.0000 | 1.2050 | 0.0000 | 0.3700 | 0.6700 | Alasso | 0.3074 | 3.0000 | 0.3500 | 0.0000 | 0.7300 | 0.2700 | SCAD | 0.2715 | 3.0000 | 1.0650 | 0.0000 | 0.4400 | 0.5600 | MCP | 0.3041 | 3.0000 | 0.5650 | 0.0000 | 0.5800 | 0.4200 | Dantzig | 0.4623 | 3.0000 | 0.6546 | 0.0000 | 0.5700 | 0.4300 | Bayesian | 0.3548 | 3.0000 | 0.5732 | 0.0000 | 0.6300 | 0.3700 | Atan | 0.2550 | 3.0000 | 0.1750 | 0.0000 | 0.8450 | 0.1550 |
| , | Lasso | 0.6027 | 3.0000 | 1.0700 | 0.0000 | 0.3550 | 0.6450 | Alasso | 0.2781 | 3.0000 | 0.1600 | 0.0000 | 0.8650 | 0.1350 | SCAD | 0.2900 | 3.0000 | 0.8550 | 0.0000 | 0.5250 | 0.4750 | MCP | 0.2752 | 3.0000 | 0.3650 | 0.0000 | 0.6850 | 0.3150 | Dantzig | 0.3863 | 3.0000 | 0.8576 | 0.0000 | 0.4920 | 0.5080 | Bayesian | 0.2563 | 3.0000 | 0.4754 | 0.0000 | 0.7150 | 0.2850 | Atan | 0.2508 | 3.0000 | 0.1000 | 0.0000 | 0.9050 | 0.0950 |
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