Research Article
Variable Selection and Parameter Estimation with the Atan Regularization Method
Table 3
Simulation results for linear regression models of Example
9.
| Method | MRME | Number of zeros | Proportion of | C | IC | Underfit | Correct-fit | Overfit |
| , | Lasso | 0.9955 | 4.9900 | 2.0100 | 0.0100 | 0.1150 | 0.8750 | Alasso | 0.5740 | 4.8650 | 0.1100 | 0.1350 | 0.8150 | 0.0500 | SCAD | 0.5659 | 4.9800 | 0.5600 | 0.0200 | 0.5600 | 0.4200 | MCP | 0.6177 | 4.9100 | 0.1200 | 0.0900 | 0.8250 | 0.0850 | Dantzig | 0.6987 | 4.8900 | 0.6700 | 0.1100 | 0.4360 | 0.4540 | Bayesian | 0.5656 | 4.8650 | 0.2500 | 0.1350 | 0.6340 | 0.2310 | Atan | 0.5447 | 4.8900 | 0.1150 | 0.1100 | 0.8250 | 0.0650 |
| , | Lasso | 1.2197 | 5.0000 | 2.0650 | 0.0000 | 0.1400 | 0.8600 | Alasso | 0.4458 | 4.9950 | 0.0900 | 0.0050 | 0.9250 | 0.0700 | SCAD | 0.4481 | 5.0000 | 0.4850 | 0.0000 | 0.6350 | 0.3650 | MCP | 0.4828 | 5.0000 | 0.1150 | 0.0000 | 0.8950 | 0.1050 | Dantzig | 0.7879 | 5.0000 | 0.5670 | 0.0000 | 0.3200 | 0.6800 | Bayesian | 0.4237 | 5.0000 | 0.1800 | 0.0000 | 0.7550 | 0.2450 | Atan | 0.4125 | 4.9950 | 0.0250 | 0.0050 | 0.9700 | 0.0250 |
| , | Lasso | 1.2004 | 5.0000 | 2.5700 | 0.0000 | 0.0900 | 0.9100 | Alasso | 0.3156 | 5.0000 | 0.0700 | 0.0000 | 0.9300 | 0.0700 | SCAD | 0.3219 | 5.0000 | 0.6550 | 0.0000 | 0.5950 | 0.4050 | MCP | 0.3220 | 5.0000 | 0.0750 | 0.0000 | 0.9300 | 0.0700 | Dantzig | 0.5791 | 5.0000 | 0.5470 | 0.0000 | 0.3400 | 0.6600 | Bayesian | 0.3275 | 5.0000 | 0.2800 | 0.0000 | 0.6750 | 0.3250 | Atan | 0.3239 | 5.0000 | 0.0750 | 0.0000 | 0.9350 | 0.0650 |
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