Table 3.

Summary statistics for the estimation of Model (A) λ(t | X₁, X₂) = λ₀(t) exp(β₁X₁ + β₂X₂) when ρ = 1.5.

Proportion censored			β1			β2			ρ		Λ(0.3)			Λ(0.7)
	n	Coef	Bias	ES	RE	Bias	ES	RE	Bias	ES	Bias	ES	RE	Bias	ES	RE
0%	100	PL	-1	12	–	2	22	–	–	–	0	3	–	0	10	–
		DEL	0	5	5.99	-22	15	1.88	–	–	4	3	0.76	15	7	1.92
		DELρ	-1	5	4.71	1	22	1.02	8	38	0	3	1.02	0	10	1.03
	400	PL	0	6	–	1	11	–	–	–	0	1	–	0	5	–
		DEL	2	2	6.26	-21	8	1.81	–	–	4	2	0.88	15	3	1.90
		DELρ	1	2	5.68	0	11	1.02	2	17	0	1	1.04	0	5	1.05
30%	100	PL	-2	14	–	2	27	–	–	–	0	3	–	0	12	–
		DEL	1	5	8.39	-25	19	1.99	–	–	4	3	0.82	16	8	2.01
		DELρ	0	2	5.20	0	11	1.01	2	17	0	1	1.03	0	5	1.04
	400	PL	-1	7	–	1	13	–	–	–	0	2	–	0	5
		DEL	1	2	7.59	-23	9	1.92	–	–	4	2	0.90	15	4	1.88
		DELρ	0	2	7.01	0	13	1.02	2	19	0	1	1.04	0	5	1.04
50%	100	PL	-2	16	–	2	32	–	–	–	0	3	–	0	13	–
		DEL	1	5	8.84	-28	22	2.09	–	–	4	3	0.97	16	10	1.84
		DELρ	0	6	8.29	1	31	1.04	12	50	0	3	1.02	1	13	1.03
	400	PL	-1	8	–	1	15	–	–	–	0	2	–	0	6	–
		DEL	2	2	9.99	-27	11	2.01	–	–	4	2	0.90	16	4	1.96
		DELρ	1	2	9.39	0	15	1.02	2	21	0	2	1.05	0	6	1.04

NOTE: β1 and β₂ are the regression coefficients, where the true parameter values are (−0.5, 0.5); Λ(t) = t² is the baseline cumulative hazard function evaluated at t; PL, the maximum partial likelihood estimator β̂_PL; DEL, the double empirical likelihood estimator β̂; DEL_ρ, the extended double empirical likelihood estimator β̂_ρ that allows for a different baseline hazard function for the aggregate data; Bias and ES, empirical bias (×100) and empirical standard deviation (×100) of 1,000 regression parameter estimates; RE, the empirical variance of the maximum partial likelihood estimator divided by that of the double empirical likelihood estimators.