Table 2 Performance of different GLM methods for Poisson regression over 100 simulations, where values in the parenthesis are the standard deviations, and ANSF: Average number of selected features; rMSE: 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
PMS | glmnet | SparseReg | L 0ADRIDGE | ||
|---|---|---|---|---|---|
L 1 | SCAD | MC+ | |||
rMSE | 1.10(±.091) | 1.090(±.092) | 1 . 0 8 7 ( ± . 0 9 1 ) | 1.937(±.222) | |
N =100 | \(|\hat { {\beta }} - {\beta } |\) | 1.755(±.274) | 1.754(±.275) | 1.737±.273) | 0 . 2 2 2 ( ± . 1 1 6 ) |
P =100 | ANSF | 43.03(±3.52) | 43.07(±3.57) | 42.06(±3.51) | 3 . 9 9 ( ± . 1 0 0 ) |
PTM | 0% | 0% | 0% | 9 9 % | |
FDR | 90.6% | 90.6% | 90.6% | 0 % | |
rMSE | 0.503(±.017) | 0.502(±.017) | 0 . 5 0 1 ( ± . 0 1 8 ) | 2.108(±.359) | |
N =100 | \(|\hat { {\beta }} - {\beta } |\) | 2.671(±.421) | 2.673(±.425) | 2.821±2.012) | 0 . 4 2 4 ( ± . 3 5 0 ) |
P=103 | ANSF | 75.47(±5.61) | 75.82(±5.71) | 75.14(±8.69) | 3 . 6 1 0 ( ± . 6 0 1 ) |
PTM | 0% | 0% | 0% | 6 4 % | |
FDR | 94.7% | 94.7% | 94.6% | 2 . 4 % | |
rMSE | 0 . 2 7 1 ( ± . 0 0 4 ) | 0.272(±.012) | 0.275(±.025) | 1.916(±.081) | |
N =500 | \(|\hat { {\beta }} - {\beta } |\) | 5.845(±.280) | 6.185(±2.359) | 5.807±.273) | 0 . 0 8 6 ( ± . 0 3 3 ) |
P=104 | ANSF | 465.6(±14.1) | 475.1(±15.5) | 463.6(±13.9) | 4 . 0 0 0 ( ± . 0 0 0 ) |
PTM | 0% | 0% | 0% | 1 0 0 % | |
FDR | 99.1% | 99.2% | 99.1% | 0 % | |