Table 1.

Simulation results for the low-dimensional data (p = 100): We used the proposed method, denoted by (A), Bayesian Cox method, denoted by (B), Cox lasso method, denoted by (C), and Cox method with the approximated information criterion, denoted by (D).

			$\left(\mathrm{A}\right)$			$\left(\mathrm{B}\right)$			$\left(\mathrm{C}\right)$			$\left(\mathrm{D}\right)$
n	p	$\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{o}\mathrm{r}$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$
1000	100	$20\mathrm{\%}$	0.13	0.88	0.12	34.80	0.00	1.00	0.72	0.57	0.43	3.59	0.08	0.92
			(0.13)	(0.11)	(0.11)	(21.64)	(0.00)	(0.00)	(1.29)	(0.25)	(0.25)	(5.09)	(0.07)	(0.07)
		$30\mathrm{\%}$	0.06	0.94	0.06	32.07	0.00	1.00	0.73	0.62	0.38	2.86	0.12	0.88
			(0.06)	(0.06)	(0.06)	(16.83)	(0.00)	(0.00)	(1.86)	(0.24)	(0.24)	(4.22)	(0.11)	(0.11)
		$40\mathrm{\%}$	0.11	0.89	0.11	28.11	0.00	1.00	0.52	0.69	0.31	2.86	0.09	0.91
			(0.10)	(0.10)	(0.10)	(21.07)	(0.00)	(0.00)	(1.02)	(0.22)	(0.22)	(4.88)	(0.08)	(0.08)
3000	100	$20\mathrm{\%}$	0.05	0.95	0.05	58.11	0.00	1.00	0.12	0.92	0.08	2.81	0.05	0.95
			(0.05)	(0.05)	(0.05)	(19.59)	(0.00)	(0.00)	(0.21)	(0.07)	(0.07)	(2.80)	(0.05)	(0.05)
		$30\mathrm{\%}$	0.08	0.93	0.07	54.35	0.00	1.00	0.08	0.93	0.07	3.03	0.05	0.95
			(0.09)	(0.07)	(0.07)	(24.96)	(0.00)	(0.00)	(0.09)	(0.07)	(0.07)	(3.42)	(0.05)	(0.05)
		$40\mathrm{\%}$	0.11	0.90	0.10	53.19	0.00	1.00	0.14	0.92	0.08	3.32	0.05	0.95
			(0.12)	(0.09)	(0.09)	(19.85)	(0.00)	(0.00)	(0.38)	(0.07)	(0.07)	(3.92)	(0.05)	(0.05)

Note: ${\hat{E}}_{IN}$ is the expected number of the incorrect nonzero covariates, ${\hat{P}}_C$ is the empirical probability of the correct model, ${\hat{P}}_O$ is the empirical probability of the overfitted model, n is the sample size, p is the number of the covariates, and censor denotes the censoring rate. The variances are provided in parentheses.