Table 2.

Simulation results for the high-dimensional data (p = 4000): We used the proposed method, denoted by (A), Bayesian Cox method, denoted by (B), and Cox lasso method, denoted by (C).

			$\left(\mathrm{A}\right)$			$\left(\mathrm{B}\right)$			$\left(\mathrm{C}\right)$
n	p	$\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{o}\mathrm{r}$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$	${\hat{E}}_{IN}$	${\hat{P}}_C$	${\hat{P}}_O$
1000	4000	$20\mathrm{\%}$	0.03	0.97	0.03	553.48	0.00	1.00	1.91	0.41	0.59
			(0.03)	(0.03)	(0.03)	(185.93)	(0.00)	(0.00)	(8.37)	(0.24)	(0.24)
		$30\mathrm{\%}$	0.04	0.96	0.04	492.63	0.00	1.00	1.77	0.50	0.50
			(0.04)	(0.04)	(0.04)	(183.71)	(0.00)	(0.00)	(11.55)	(0.25)	(0.25)
		$40\mathrm{\%}$	0.02	0.98	0.02	427.08	0.00	1.00	1.88	0.44	0.56
			(0.02)	(0.02)	(0.02)	(138.11)	(0.00)	(0.00)	(8.81)	(0.25)	(0.25)
3000	4000	$20\mathrm{\%}$	0.08	0.93	0.07	1695.04	0.00	1.00	0.68	0.67	0.33
			(0.09)	(0.07)	(0.07)	(406.50)	(0.00)	(0.00)	(1.57)	(0.22)	(0.22)
		$30\mathrm{\%}$	0.09	0.92	0.08	1562.78	0.00	1.00	0.56	0.76	0.24
			(0.10)	(0.07)	(0.07)	(497.41)	(0.00)	(0.00)	(2.31)	(0.18)	(0.18)
		$40\mathrm{\%}$	0.13	0.88	0.12	1415.71	0.00	1.00	0.40	0.83	0.17
			(0.13)	(0.11)	(0.11)	(382.23)	(0.00)	(0.00)	(3.17)	(0.14)	(0.14)

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 of each member are provided below in parentheses.