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. Author manuscript; available in PMC: 2012 Aug 15.
Published in final edited form as: Stat Med. 2011 May 12;30(18):2326–2340. doi: 10.1002/sim.4268

Table 5.

Simulation results for Experiment 3: 15% non-zero coefficients, ρ = 0.4, n = 600.

The Poisson Component
Method MRMSEa Specificityb Sensitivityc Under fitd Exact fite Over fitf
1 2 ≥3
30% zeros
PR-LASSO 1.344 0.655 1.000 0.000 0.000 0.001 0.026 0.973
PR-SCAD 1.439 0.723 1.000 0.000 0.014 0.043 0.088 0.855
ZIP-LASSO 0.391 0.961 1.000 0.000 0.538 0.290 0.121 0.051
ZIP-SCAD 0.080 0.948 1.000 0.000 0.652 0.092 0.084 0.172

45% zeros
PR-LASSO 2.864 0.568 1.000 0.000 0.000 0.000 0.001 0.999
PR-SCAD 3.647 0.652 1.000 0.000 0.000 0.001 0.015 0.984
ZIP-LASSO 0.372 0.953 1.000 0.000 0.479 0.316 0.126 0.079
ZIP-SCAD 0.147 0.876 1.000 0.000 0.202 0.183 0.209 0.406

The Zero Component
30% zeros
ZIP-LASSO 1.832 1.000 0.000 1.000 0.000 0.000 0.000 0.000
ZIP-SCAD 1.508 1.000 0.192 0.950 0.049 0.001 0.000 0.000

45% zeros
ZIP-LASSO 2.640 1.000 0.000 1.000 0.000 0.000 0.000 0.000
ZIP-SCAD 0.794 0.998 0.655 0.680 0.309 0.009 0.002 0.000
a

MRMSE = Median of the ratio of the reduced model MSE to the full model MSE.

b

Specificity = Mean of the proportion of zero coefficients that were correctly identified.

c

Sensitivity = Mean of the proportion of nonzero coefficients that were correctly identified.

d

Under fit = Probability of excluding any significant coefficients.

e

Exact fit = Probability of selecting the exact sub-model.

f

Over fit = Probabilities of including all significant variables and some noise variables (1, 2, ≥ 3).