Table III.

Average mean square error value of parameters for each method.

	True value		Case 6 EMGTS	EMMCEM
π2	0.3333	est.	0.321	0.334
		mse	0.002	0.002
t50	100	est.	100.294	99.270
		mse	4.371	4.391
σt50	60	est.	67.842	68.433
		mse	128.657	137.873
β	2	est.	1.804	1.736
		mse	0.130	0.164
σβ	1	est.	0.826	0.814
		mse	0.059	0.0580
γ	1.5	est.	1.731	1.761
		mse	0.168	0.135
σγ	1	est.	0.972	0.959
		mse	0.111	0.109
η	0.5	est.	0.711	0.720
		mse	0.273	0.275
ση	0.15	est.	0.211	0.232
		mse	0.037	0.036
σε	0.1	est.	0.099	0.098
		mse	0.00006	0.00005
cov(t50, β)	ρ	est.	0.075	0.072
		mse	1.041	1.030
cov(t50, γ)	ρ	est.	−0.045	−0.047
		mse	0.040	0.0398
cov(t50, η)	0.15ρ	est.	−0.010	−0.011
		mse	0.009	0.009
cov(β, γ)	ρ	est.	0.315	0.141
		mse	0.566	0.578
cov(β, η)	0.15ρ	est.	0.151	0.153
		mse	0.025	0.023
cov(γ, η)	0.15ρ	est.	0.121	0.130
		mse	0.026	0.032

In this simulation, the individuals θ_j = (t_50j, β_j, γ_j, η_j) were generated from the multivariate normal distributions with mean (100, 2, 1.5, 0.5)^T and covariance matrix Σ; Case 6: ${\pi }_1=\frac{14}{21},{\pi }_2=\frac{7}{21}$, σ_t50 = 60, σ_ε = 0.1, and a compound symmetric covariance matrix Σ with ρ = 0.1. EMGTS is a method based on the mixture of EM and GTS; EMMCEM is a method based on the mixture of EM and MCEM.