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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

From: Sparse generalized linear model with L 0 approximation for feature selection and prediction with big omics data

  PMS L 1 SparseReg L 0ADRIDGE
    SCAD MC+  
  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 )
  PTM 0% 0% 2% 8 1 %
  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 )
  PTM 0% 0% 0% 8 0 %
  FDR 90.5% 93% 83.9% 7 . 3 %
  1. PMS: Performance Measures. PTM: Percentage of true models. FDR: False discovery rate. The values in boldface indicate the best performance