Table 3 Performance of different GLM methods for logistic regression over 100 simulations, where ANSF: Average number of selected features; trMSE: Test Average square root of mean squared error; \(|\hat { {\beta } }- {\beta } | =\sum _{i} |\hat { {\beta } }_{i} - {\beta }_{i}|\): average absolute bias when comparing true and estimated parameters
| Â | PMS | L 1 | SparseReg | L 0ADRIDGE | |
|---|---|---|---|---|---|
| Â | Â | Â | SCAD | MC+ | Â |
|  | trMSE | 0.0474(±.0035) | 0.0469(±.0039) | 0.0456(±.0042) | 0 . 0 4 3 4 ( ± . 0 0 2 8 ) |
N =100 | \(|\hat { {\beta }} - {\beta } |\) | 0.2984(±.1262) | 0.3129(±.1249) | 0.1625(±.0752) | 0 . 0 6 8 2 ( ± . 0 4 1 6 ) |
P =100 | ANSF | 17.10(±9.32) | 18.35(±10.185) | 10.410(±6.174) | 3 . 3 3 0 ( ± . 7 7 9 ) |
| Â | PTM | 0% | 0% | 2% | 8 1 % |
| Â | FDR | 77.7% | 78.4% | 62.4% | 6 . 6 % |
|  | trMSE | 0.0517(±.0045) | 0.0496(±.0046) | 0.0468(±.0045) | 0 . 0 4 3 4 ( ± . 0 0 3 0 ) |
N =100 | \(|\hat { {\beta }} - {\beta } |\) | 0.5968(±.2599) | 0.6465(±.2205) | 0.2818(±.1030) | 0 . 0 7 5 4 ± . 0 6 0 0 ) |
P=1000 | ANSF | 50.92(±39.974) | 73.030(±40.792) | 24.80(±13.314) | 3 . 4 1 ( ± 1 . 0 6 5 ) |
| Â | PTM | 0% | 0% | 0% | 8 0 % |
| Â | FDR | 90.5% | 93% | 83.9% | 7 . 3 % |