Table 3.

Result of Scenario II for n = 200, p*= 0.05 and m = 10. L-logistic — results from the linear determination rule with logistic loss approximation; L-hinge — results from the linear determination rule with hinge loss approximation; plug-in-rf— results from plug-in personalized rule with random forest fitting; plug-in-lg — results from plug-in personalized rule with logistic fitting; plug-in-cv — selecting between plug-in-rf and plug-in-lg with cross-validation.

Method	β0	${\text{se}}_{{\beta }_0}$	β1	${\text{se}}_{{\beta }_1}$	β2	${\text{se}}_{{\beta }_2}$	β3	${\text{se}}_{{\beta }_3}$	TPF	seTPF	FPF	seFPF
α = 0.3 for ‘rule in’
Standard	−0.996	0.090	−0.064	1.463	−0.160	1.742	−0.078	2.260	0.221	0.107	0.220	0.109
plug-in-rf									0.763	0.055	0.293	0.054
plug-in-lg									0.213	0.114	0.214	0.118
plug-in-cv									0.763	0.055	0.293	0.054
L-logistic	−0.996	0.090	0.336	0.131	0.347	0.121	−0.002	0.167	0.715	0.088	0.312	0.063
L-hinge	−1.000	0.000	0.344	0.235	0.333	0.283	−0.013	0.292	0.704	0.107	0.308	0.063
γ = 0.1 for ‘rule out’
Standard	1.000	0.000	−0.016	0.275	−0.004	0.277	0.006	0.379	0.957	0.045	0.956	0.044
plug-in-rf									0.900	0.037	0.524	0.099
plug-in-lg									0.960	0.045	0.959	0.045
plug-in-cv									0.900	0.037	0.524	0.099
L-logistic	−0.456	0.890	0.325	0.333	0.325	0.340	−0.011	0.232	0.857	0.049	0.614	0.198
L-hinge	−0.970	0.243	0.554	0.179	0.560	0.169	−0.003	0.178	0.881	0.037	0.580	0.077